Google Data Analytics Professional Certification Practice Test

In data analysis, why is data quality important?

1. It affects the speed of data retrieval

2. It determines the complexity of data modeling

3. It impacts the reliability and accuracy of insights

4. It influences the types of data visualizations used

The correct answer is: It impacts the reliability and accuracy of insights

Data quality is critical in data analysis because it directly impacts the reliability and accuracy of the insights drawn from the data. High-quality data ensures that the findings and conclusions are based on accurate representations of reality. When data is trustworthy, analysts can confidently make decisions and recommendations based on the analysis. Poor data quality, on the other hand, can lead to misleading conclusions, which may have significant consequences for businesses and decision-making processes. While aspects such as the speed of data retrieval, complexity of data modeling, and types of data visualizations can be influenced by data quality, the most fundamental issue remains the integrity of the insights derived from the data. Without reliable data, any analysis could misrepresent the actual patterns and trends, thus undermining the entire analytical process. Consequently, maintaining high data quality is essential for achieving accurate and actionable insights, which is a primary goal of data analysis.