Effective data cleansing involves several key steps:
Data Profiling: Assess the current state of the data to identify quality issues and understand the scope of cleansing required. Data Standardization: Ensure consistency by applying uniform formats, naming conventions, and units of measure. Data Deduplication: Identify and remove duplicate records to prevent redundancy. Data Validation: Verify data accuracy by cross-referencing with reliable sources. Data Enrichment: Supplement incomplete data with additional information to ensure comprehensiveness. Data Audit: Regularly review the data cleansing process to identify areas for improvement and ensure ongoing data quality.