What does the term "data cleansing" refer to?

Data cleansing refers to the process of detecting and correcting data errors to ensure the accuracy and quality of the data. This is a critical aspect of data management, as inaccurate or inconsistent data can lead to erroneous analysis and decision-making. During data cleansing, various tasks may be performed, such as identifying duplicate records, correcting typographical errors, standardizing data formats, and filling in missing values. The goal is to improve the reliability of the data, making it suitable for analysis, reporting, and operational use.

Other options focus on different aspects of data management. Minimizing storage costs involves optimizing how data is stored, while enhancing data security pertains to protecting data from unauthorized access and breaches. Creating data backups is crucial for data recovery but does not involve correcting errors within the data itself. All these aspects are important in their own right but do not capture the essence of data cleansing.