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Data Cleansing
Data cleansing, also referred to as data cleaning or scrubbing, is the process of identifying and resolving errors, inconsistencies, and inaccuracies in datasets. This ensures data is accurate, complete, and ready for reliable analysis. Typical tasks include correcting typos, standardizing formats, removing duplicate records, and filling in missing values. By ensuring data quality, organizations can derive meaningful insights, improve predictions, and make informed decisions. Clean data also helps reduce errors in operations and optimizes workflows. Industries like healthcare, finance, and marketing heavily rely on data cleansing to maintain efficiency and accuracy in their processes. Maintaining high-quality datasets is critical for organizations seeking to enhance productivity and achieve consistent, successful outcomes.