What are two benefits of good data quality management in improving business decision-making? (Choose 2 answers.)

While good data management can streamline processes, it does not inherently speed up the initiation of statistical analysis. The focus of data quality management is on ensuring the accuracy and reliability of data rather than the speed at which analysis begins. Thus, this choice does not accurately reflect the benefits of good data quality management.

Good data quality management strives to minimize missing data, but it cannot guarantee that there will be absolutely no missing data points. Data quality management involves techniques to handle missing data effectively, but the presence of missing data is often an unavoidable reality in many datasets.

Data quality management does not guarantee statistical significance of a sample; rather, it ensures that the data is accurate and reliable. Statistical significance depends on the sample size and the actual data distribution, not merely on data quality practices. Thus, this statement misrepresents the role of data quality management.