Data Cleansing Techniques for Operational Database Integration

Abstract

Operational database integration requires accurate, consistent, and standardized data to support reliable information sharing across enterprise applications. Data cleansing techniques play a critical role in detecting, correcting, and transforming incomplete, duplicated, inconsistent, or incorrectly formatted records before integration. Existing literature highlights rule-based validation, duplicate detection, missing value handling, data standardization, schema matching, and entity resolution as important methods for improving database quality. However, many organizations still face challenges such as heterogeneous data formats, legacy database errors, inconsistent naming conventions, redundant customer or transaction records, and weak validation controls during integration. This research is important because poor-quality operational data can affect reporting accuracy, transaction processing, system interoperability, and managerial decision-making. This article discusses data cleansing techniques for operational database integration, focusing on error detection, normalization, duplicate removal, missing value treatment, referential integrity checking, and validation workflows. The study concludes that effective data cleansing improves data reliability, reduces integration failures, strengthens database consistency, and supports more accurate enterprise-level information processing.