Data scrubbing, also known as data cleansing, is the process of identifying and correcting errors or inconsistencies in a dataset. This ensures that the information is accurate, complete, and reliable for analysis. Common issues addressed during data scrubbing include duplicate entries, incorrect formatting, and missing values. The importance of data scrubbing lies in its ability to improve the quality of data used in decision-making. By maintaining clean data, organizations can enhance their data analysis efforts, leading to better insights and more effective strategies. This practice is essential in various fields, including business intelligence and machine learning.