Machine Learning for Ensuring Data Integrity in Salesforce Applications

Abstract

This study investigates the application of machine learning algorithms to enhance data integrity within Salesforce applications, addressing the challenge of detecting anomalies in complex CRM datasets. The research aims to evaluate the effectiveness of various machine learning models—specifically Isolation Forest, One-Class SVM, and Autoencoders—in identifying data irregularities and improving overall data quality. Utilizing a dataset of 10,000 records from Salesforce, the study involved preprocessing the data, implementing the ML models, and assessing their performance using metrics such as Precision, Recall, and F1 Score. Major findings indicate that machine learning models significantly outperform traditional anomaly detection methods, with Autoencoders demonstrating superior performance in handling high-dimensional data. The implementation of these models resulted in notable improvements in data accuracy and reduced error rates. The study concludes that integrating machine learning into CRM systems can substantially enhance data integrity, offering valuable insights for both theoretical research and practical applications. Future research should explore additional algorithms and real-world deployment challenges.