51. What are two benefits of good data quality management in improving business decision-making?
Answer: A,B
Good data quality management mitigates errors and ensures completeness.
Effective data quality management significantly mitigates undetected errors from the data-entry process and ensures there are no missing data points, both of which enhance the reliability of business decision-making.
A) It mitigates undetected errors from the data-entry process.
This option is correct as good data quality management directly addresses the issues arising from human error during data entry. By implementing rigorous quality checks and validation processes, organizations can reduce the likelihood of inaccuracies that can lead to poor decision-making.
B) It ensures there are no missing data points.
This option is also correct because ensuring completeness of data is crucial for accurate analysis. Missing data can skew results and lead to misguided conclusions, so effective management practices that focus on data integrity help to maintain a comprehensive dataset necessary for informed decision-making.
C) It begins the statistical process faster.
This option is incorrect because while good data quality management may streamline processes, it does not inherently speed up the initiation of statistical analysis. The focus is instead on ensuring the accuracy and completeness of the data before any analysis can occur.
D) It guarantees that a sample will be statistically significant.
This option is incorrect as good data quality management does not guarantee statistical significance. While high-quality data can improve the reliability of results, significance depends on various factors including sample size and variability, which are not directly addressed by data quality measures.
Conclusion
In summary, options A and B highlight the essential benefits of good data quality management by focusing on error mitigation and the completeness of data. These factors are critical for enabling sound business decision-making, while the other options either misrepresent the outcomes of data quality management or address unrelated concepts.