Data Deduplication Techniques in Customer Databases

Abstract

Data deduplication techniques are important in customer databases because they help identify and remove repeated, inconsistent, or partially matching customer records stored across enterprise systems. Customer data often becomes duplicated due to multiple registration channels, spelling variations, incomplete entries, address changes, and system integration errors. Existing literature highlights exact matching, fuzzy matching, phonetic matching, rule-based comparison, similarity scoring, entity resolution, and record linkage as major approaches for detecting duplicate customer records. However, many organizations still face challenges such as inconsistent customer names, different address formats, missing contact details, false matches, fragmented identifiers, and poor synchronization between operational databases. This research is important because duplicate customer records can affect customer relationship management, marketing accuracy, billing reliability, compliance reporting, and decision-making. This article discusses data deduplication techniques in customer databases, focusing on data profiling, standardization, matching rules, similarity measurement, duplicate clustering, merge strategies, and validation workflows. The study concludes that effective deduplication improves customer data accuracy, reduces redundancy, strengthens enterprise data quality, and supports more reliable customer analytics.