Data cleaning, also known as data cleansing or scrubbing, is detecting and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality and reliability for analysis and decision-making purposes. This process involves identifying missing values, handling outliers, resolving duplicates, standardizing formats, and removing irrelevant or redundant data. By cleaning data, organizations can ensure that their data is accurate, complete, and consistent, leading to more reliable insights and conclusions.