Which of the following is an important step in data preparation?

Data cleaning is a crucial step in data preparation because it ensures the integrity, accuracy, and quality of the data before any analysis takes place. This process involves identifying and correcting errors, inconsistencies, and inaccuracies within the dataset, which can arise for various reasons, such as incorrect data entry, missing values, or format discrepancies. By thoroughly cleaning the data, analysts can significantly reduce the likelihood of generating misleading insights or conclusions based on poor-quality data.

Effective data cleaning often includes tasks such as removing duplicates, filling in missing values through imputation methods, normalizing formats, and addressing outliers. These actions help to create a dataset that truly reflects the underlying information and enhances the reliability of data-driven decisions.

The other options, while still relevant to the overall data analysis process, do not represent foundational steps in data preparation. Data visualization, for instance, is typically performed after data preparation to help communicate findings, while data storage and data reporting pertain to how data is organized and presented after it has been cleaned and analyzed.