What is a primary goal of data quality (DQ) drivers?

What is a primary goal of data quality (DQ) drivers?

• A. Increase the number of data sources available
• B. Reduce costs associated with data storage
• C. Improve efficiency and productivity while reducing risks
• D. Enhance the aesthetic value of data presentations

Answer: (C)

Improving efficiency and productivity while reducing risks is a primary goal of data quality (DQ) drivers because high-quality data enables organizations to make informed decisions, streamline processes, and avoid errors that could lead to financial losses or reputational damage. When data is accurate, consistent, and reliable, organizations can rely on it for critical operations, thereby enhancing overall productivity. Additionally, high-quality data supports risk management efforts by providing trustworthy information, which helps in identifying potential issues before they escalate. This proactive approach reduces uncertainty in decision-making and contributes to better outcomes in various business processes. As a result, focusing on data quality directly supports organizational goals related to operational efficiency and risk mitigation. The other options, while they may contribute to various aspects of data management, do not directly align with the fundamental purpose of DQ drivers. For example, simply increasing the number of data sources or reducing storage costs does not inherently improve the quality or reliability of the data. Likewise, enhancing aesthetic value alone does not contribute to the functional effectiveness or usability of data.

Improving efficiency and productivity while reducing risks is a primary goal of data quality (DQ) drivers because high-quality data enables organizations to make informed decisions, streamline processes, and avoid errors that could lead to financial losses or reputational damage. When data is accurate, consistent, and reliable, organizations can rely on it for critical operations, thereby enhancing overall productivity.

Additionally, high-quality data supports risk management efforts by providing trustworthy information, which helps in identifying potential issues before they escalate. This proactive approach reduces uncertainty in decision-making and contributes to better outcomes in various business processes. As a result, focusing on data quality directly supports organizational goals related to operational efficiency and risk mitigation.

The other options, while they may contribute to various aspects of data management, do not directly align with the fundamental purpose of DQ drivers. For example, simply increasing the number of data sources or reducing storage costs does not inherently improve the quality or reliability of the data. Likewise, enhancing aesthetic value alone does not contribute to the functional effectiveness or usability of data.