Question 14

Good data quality management ensures there are no missing data points and mitigates undetected errors from the data-entry process.

Good data quality management is essential in improving business decision-making as it ensures there are no missing data points and mitigates undetected errors from the data-entry process.

A) It begins the statistical process faster.

While starting the statistical process quickly can be beneficial, it does not directly relate to the quality of data management. Good data quality focuses on the accuracy and completeness of data rather than the speed of analysis.

B) It guarantees that a sample will be statistically significant.

Statistical significance is determined by the relationship between the sample size and the population from which it is drawn, rather than directly by data quality. Therefore, this option does not accurately represent a benefit of good data quality management.

C) It ensures there are no missing data points.

Ensuring there are no missing data points is a key benefit of good data quality management. Complete datasets provide a more reliable basis for analysis, leading to better-informed business decisions.

D) It mitigates undetected errors from the data-entry process.

Mitigating undetected errors from the data-entry process is another significant benefit of good data quality management. By reducing errors, businesses can enhance the accuracy of their data, which is crucial for effective decision-making.

Conclusion

In summary, the correct answer highlights that good data quality management ensures no missing data points and reduces errors during data entry, both of which are vital for reliable business decision-making. Other options fail to address the core aspects of data quality management, illustrating why they do not apply in this context.