Abstract
Master Data Management concept came into the mainstream during the mid 2000s, to generate a single Golden profile of the customer. As the Enterprise IT architecture started adopting and integrating various Commercial Products for CRM, ERP, Finance, HR, Supply Change Management etc. along with other homegrown custom apps, the information around the Customer started drifting across the various applications which resulted in various inefficiencies in day-to-day business operation. Master Data Management tools and technologies provide a way to perform identity resolution and survive latest and greatest information. Traditionally, the MDM tools always adopt a lean approach that uses minimal attributions to identify the smallest amount of (master) data with the biggest influence on business outcomes like Name, Phone, SSN, Email and Address. These constitute only less than 1% of the enterprise data, In the age of generative AI, there is a greater need to understand the context and the relationship of ALL data. This paper explores an alternate approach to mitigate the shortcomings of the currently available MDM tools
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Schedule a callMore From: Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
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