Hybrid framework for master data management

Abstract

Master data management and real-time data warehousing are gaining increased prominence within the worlds of business and technology. Past research efforts have shed light towards development of many master data management approaches. On the other hand, growth of technology has demanded real-time analytics and real-time processing of data. This trend has shed light in developing multiple real-time data warehousing approaches to perform real-time analytics based on an organization's requirements. With the evolution of real-time data warehousing, Master Data Management was an issue for large organizations' when both systems are working in the same business environment. Since both systems focus on real-time integration, similar, duplicated and parallel data extraction processes were executed by these applications. This was due to the fact that master Data Management was designed to focus on the operational aspect and real-time data warehouse was designed to focus on analysis aspect of the organization. Hence, each had its own ways of managing master data. These duplicated extractions caused data quality issues in these parallel applications. This research provides a framework that combines both master data management and real-time data warehousing and ultimately proposing to build a Hybrid Real-Time Data Warehousing Architecture in order to achieve enterprise wide master data management.