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Data cleansing (or data scrubbing) is the process of identifying and correcting corrupt, inaccurate, or irrelevant records from a record set, table, or database. In the context of data management and AI training, it is the most critical step for ensuring that downstream analysis or model outputs are reliable. Poor data quality often leads to "Garbage In, Garbage Out," where even the most advanced algorithms produce flawed results due to noisy or biased input. The Data Cleansing Workflow A standard data cleansing process typically follows these functional steps: 1. Data Auditing & Profiling Before cleaning, you must understand the "health" of your data. This involves using statistical summaries to detect outliers, missing values, and structural inconsistencies. 2. Standardizing and Normalizing Data often comes from disparate sources with different fo...