Identify and fix various types of data anomalies – from missing, incomplete, duplicate records to formatting, casing, and spelling errors – in batch or real-time.

Data scrubbing, also called data cleansing, is the process of identifying inconsistencies, inaccuracies, incompleteness, and other messy data and then scrubbing it to get clean and standardized data across the enterprise, especially for downstream analytics applications which support business processes and decision-making.

Data scrubbing software achieves this by first profiling data, applying standardization techniques, and then matching entities across organization-wide systems or within a dataset for enrichment and deduplication purposes.

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