What is data cleansing?

Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting or removing inaccurate, incomplete, irrelevant, redundant, or inconsistent data from a dataset. The goal is to improve the quality of the data, ensuring it is accurate, consistent, and reliable for decision-making and analysis. Common techniques include handling missing values, correcting typos, standardizing formats, and removing duplicates. Data cleansing is a critical step in data preprocessing before data warehousing, data mining, machine learning, and other data-driven processes.