AI-Driven Data Integrity: Machine learning algorithms identifying and resolving duplicate records in salesforce CRM

Panguluri, Vani (2025) AI-Driven Data Integrity: Machine learning algorithms identifying and resolving duplicate records in salesforce CRM. World Journal of Advanced Research and Reviews, 26 (2). pp. 3916-3924. ISSN 2581-9615

This article examines the implementation of artificial intelligence for data cleansing and deduplication in CRM systems, with a focus on Salesforce environments. The article explores how machine learning algorithms, natural language processing, and advanced pattern recognition techniques have revolutionized data quality management by automating error detection, standardizing fields, and intelligently consolidating duplicate records. The article presents a theoretical framework of data quality dimensions, traces the evolution of cleansing methodologies, and provides empirical analysis of business impacts across industry verticals. Through examination of fuzzy matching algorithms, confidence scoring mechanisms, and automated workflows, the article demonstrates significant improvements in data accuracy, completeness, consistency, and uniqueness following AI implementation. The article also addresses current limitations of AI approaches and identifies emerging trends such as quantum computing applications, federated learning, and graph-based data models for enhanced CRM data optimization, concluding with actionable recommendations for organizations seeking to maximize ROI from these technologies.