Data Analytics – Adaptive Reading Practice Test

The primary purpose of cleaning data in the analysis process is to ensure high-quality data. High-quality data is crucial for accurate analysis and meaningful insights. Cleaning data involves identifying and correcting errors, removing duplicates, handling missing values, and ensuring that the data is consistently formatted. This process helps eliminate inaccuracies that could lead to misleading conclusions, thus enhancing the reliability of the analysis.

While enhancing data visualization is certainly an important aspect of presenting data, it is a secondary effect of having high-quality data. Similarly, minimizing data storage and increasing data speed are objectives that may be relevant in the broader context of data management, but they do not directly relate to the essential function of cleaning data, which is to improve its quality and integrity. High-quality data serves as the foundation for all subsequent analysis, making it the central aim of the data cleaning process.