Which of the following best describes data cleansing in a data warehouse?

Data cleansing in a data warehouse is a crucial process focused on ensuring the quality and reliability of the data within the system. Standardizing and validating data for accuracy means that any inconsistencies, inaccuracies, or errors in the data are identified and corrected. This step helps to guarantee that the information stored in the data warehouse is trustworthy and can be used for analysis and decision-making without introducing biases or inaccuracies.

For example, if customer names are stored in different formats (some in uppercase, some in lowercase, and others with typos), data cleansing processes would standardize these entries into a consistent format, ensuring that analysis of customer data is accurate. Validation also involves checking for compliance with defined data standards or business rules, further enhancing the integrity of the data.

In contrast, other approaches such as collecting data from only one source do not necessarily guarantee accuracy, as that single source might contain its own errors. Removing historical records does not contribute to cleansing but rather limits the data available for analysis. Lastly, formatting data for user interface design is not directly related to data cleansing, as it pertains more to the presentation of the data rather than its accuracy and correctness. Therefore, standardizing and validating data for accuracy represents the essential focus of data cleansing in a data warehouse.