Data analysis begins with data cleansing, which is the process of identifying and correcting or removing incorrect, incomplete, duplicate, or irrelevant records from a data set. The goal of cleansing is to ensure that data is accurate, consistent, and complete so that it can be reliably used for analysis, decision-making, and artificial intelligence applications. The process involves inspecting, correcting, verifying, and reporting issues, and can be done manually or with the help of specialized tools and automation. After cleansing, the data is more reliable and ready for further processing and analysis.
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