What does 'data cleaning' involve?

Data cleaning is a critical step in data management that involves fixing or removing incorrect, incomplete, or irrelevant data from a dataset. This process ensures that the data is accurate and reliable, which is essential for any subsequent analysis or decision-making. During data cleaning, analysts may identify and correct errors, fill in missing values, standardize formats, and remove duplicates. By ensuring high data quality, organizations can trust the insights derived from their data, leading to more informed and effective decisions.

The other options represent different processes that are part of data handling and analysis but do not pertain to data cleaning specifically. For instance, creating visual representations of data relates to data visualization, reporting involves communicating analysis results, and storing data securely addresses data management and security but does not focus on data integrity challenges inherent in cleaning.