Data Cleansing Techniques for Incomplete and Inconsistent Records

Abstract

Data cleansing techniques for incomplete and inconsistent records are important because enterprise databases must maintain accurate, reliable, and usable data for transactions, reporting, analytics, and decision-making. Incomplete and inconsistent records often occur due to missing fields, manual entry errors, duplicate values, format differences, invalid codes, and weak validation during data collection. Existing literature highlights missing value treatment, data standardization, duplicate detection, format correction, rule-based validation, outlier identification, and consistency checking as major techniques for improving data quality. However, many organizations still face challenges such as fragmented records, incorrect attribute values, inconsistent naming patterns, mismatched formats, and unreliable source data. This research is important because poor-quality records can affect system integration, reporting accuracy, customer analysis, compliance monitoring, and operational efficiency. This article discusses data cleansing techniques for incomplete and inconsistent records, focusing on data profiling, missing value handling, standardization rules, error correction, duplicate removal, validation checks, and quality monitoring. The study concludes that effective data cleansing improves data accuracy, reduces errors, strengthens database reliability, and supports trusted enterprise information management.