Driving Performance Improvement of an Organization through Data Object Fusion

Abstract

To succeed in today's data-driven economy, organizations must find ways to put their massive data stores to work competitively. This research delves into the possibility of using data object fusion techniques and, more significantly, consensus clustering to boost the efficiency of businesses in an area of expertise. A case investigation of the automotive service sector demonstrates potential results and puts theoretical knowledge into practice within an organization. Therefore, this study addresses the prospective benefits of data object fusion in the automotive service sector. Furthermore, by combining the findings of different clustering methods, consensus clustering can provide a more precise and reliable outcome. Moreover, a consistent representation of the data objects is obtained by applying this technique to disparate datasets acquired from different sources inside the organization, which improves decision-making and productivity in operations. The research highlights the significance of data quality and the selection of proper clustering techniques to achieve dependable and accurate data object fusion. The findings add to the expanding knowledge of using data-driven ways to enhance organizational performance in any emerging sector.