What is the ultimate aim of defining data quality standards?

What is the ultimate aim of defining data quality standards?

• A. To increase the quantity of data collected
• B. To ensure data is clean, accurate, and trustworthy
• C. To reduce the costs associated with data storage
• D. To minimize user access to sensitive data

Answer: (B)

The ultimate aim of defining data quality standards is to ensure that data is clean, accurate, and trustworthy. Data quality standards establish criteria and benchmarks that data must meet to be considered reliable. By defining these standards, organizations can improve the integrity of their data, which is crucial for effective decision-making and operational efficiency. High-quality data supports accurate analysis, enhances customer satisfaction, and promotes trust among stakeholders, ultimately contributing to better business outcomes. While other options may touch on aspects related to data management, they do not capture the core purpose of data quality standards as effectively. For instance, increasing the quantity of data collected may lead to data overload if quality is not maintained, while reducing storage costs is more a function of data management strategies rather than a direct aim of data quality standards. Minimizing user access to sensitive data involves data security rather than data quality, which further highlights that the focus of quality standards is on the attributes of the data itself rather than its accessibility or volume.

The ultimate aim of defining data quality standards is to ensure that data is clean, accurate, and trustworthy. Data quality standards establish criteria and benchmarks that data must meet to be considered reliable. By defining these standards, organizations can improve the integrity of their data, which is crucial for effective decision-making and operational efficiency. High-quality data supports accurate analysis, enhances customer satisfaction, and promotes trust among stakeholders, ultimately contributing to better business outcomes.

While other options may touch on aspects related to data management, they do not capture the core purpose of data quality standards as effectively. For instance, increasing the quantity of data collected may lead to data overload if quality is not maintained, while reducing storage costs is more a function of data management strategies rather than a direct aim of data quality standards. Minimizing user access to sensitive data involves data security rather than data quality, which further highlights that the focus of quality standards is on the attributes of the data itself rather than its accessibility or volume.