What is the objective of "Data Cleansing" when working with the Caboodle Data Model?

The objective of "Data Cleansing" in the context of the Caboodle Data Model focuses on identifying and rectifying inaccuracies and inconsistencies within data sets. This process is essential for ensuring that the data used for analysis, reporting, and decision-making is of high quality.

Data cleansing involves various techniques to detect errors such as duplicates, missing values, incorrect formats, and other discrepancies that can lead to misleading insights. By correcting these issues, organizations can ensure that their analyses are based on reliable and accurate data, which ultimately leads to better outcomes and more informed decision-making.

Improving data quality through cleansing also enhances the overall performance of data-related operations, as clean data can significantly reduce the time spent on data manipulations and increase user trust in the data analytics process. This is crucial in a health data environment where data integrity can directly impact patient outcomes and operational efficiency.

In contrast, while enhancing data visualization, increasing data storage capacities, or creating new user accounts can be important tasks in data management, they do not directly relate to the primary aim of data cleansing, which is to improve data accuracy and consistency.