Data Cleansing and Matching in Supplier Master Databases

Abstract

Data cleansing and matching are important in supplier master databases because organizations depend on accurate supplier records for procurement, payments, compliance checks, contract management, and vendor performance analysis. Supplier databases often contain duplicate vendor names, incomplete addresses, inconsistent tax identifiers, outdated contact details, spelling variations, inactive suppliers, and mismatched banking information. Traditional manual correction may not be sufficient when supplier records are created across multiple departments, ERP modules, branches, or legacy systems. This article focuses on data cleansing and matching in supplier master databases by examining duplicate detection, standardization of supplier names, address normalization, tax and bank detail validation, fuzzy matching, rule-based comparison, and record consolidation. The study discusses how structured cleansing and matching can improve supplier data accuracy, reduce payment errors, prevent duplicate onboarding, support audit readiness, and strengthen procurement control. The article concludes that effective supplier master data cleansing improves data quality, reduces operational risk, enhances vendor governance, and supports reliable enterprise procurement and financial processes.