Detecting and Auto-correcting Deal Data Issues in Salesforce

The management of deal information in Salesforce, a top customer relationship management (CRM) platform, is important for maintaining data accuracy and enhancing decision-making processes. Nonetheless, inaccuracies, inconsistencies, and errors in the data are prevalent, which can result in erroneous sales forecasts, compromised decision-making, and lower business performance. Over the past decade (2015-2024), several strategies have been proposed to identify and automatically correct these errors, from rule-based validation frameworks to sophisticated solutions based on machine learning and artificial intelligence. Although these studies have made considerable strides in the automation of data repair, there are still gaps in the development of systems that can cope with the growing complexity of deal data across various industries. The current approaches primarily rely on pre-defined rules, machine learning algorithms, or data governance approaches; however, they fail to adapt to new, emerging types of data and dynamic environments. Moreover, the incorporation of advanced technologies such as blockchain and reinforcement learning into Salesforce for automated data validation and correction remains in an embryonic stage. This paper seeks to bridge these research gaps by exploring the application of hybrid AI models that integrate machine learning, natural language processing (NLP), and blockchain technologies to develop a more resilient, real-time, and dynamic system for deal data management.