Driving Performance Improvement of an Organization through Data Object Fusion
Lamia Alhazmi
Lamia Alhazmi, Department of Management Information System, College of Business Administration, Taif University, P.O Box 11099, Taif, 21944, Saudi Arabia.
Manuscript received on 18 May 2023 | Revised Manuscript received on 05 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 26-33 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B77360712223 | DOI: 10.35940/ijrte.B7736.0712223
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: To succeed in today’s data-driven economy, organizations must find ways to put their massive data stores to work competitively. This research explores the potential of utilising data object fusion techniques and, more significantly, consensus clustering to enhance the efficiency of businesses in their area of expertise. A case investigation of the automotive service sector yields promising results and applies theoretical knowledge in a practical setting within an organisation. 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 achieved by applying this technique to disparate datasets acquired from different sources within the organisation, which enhances decision-making and operational productivity. The research highlights the importance of data quality and the selection of suitable clustering techniques to achieve reliable and accurate data object fusion. The findings contribute to the growing body of knowledge on utilising data-driven approaches to enhance organisational performance in emerging sectors.
Keywords: Information Fusion, Clustering, Decision-Making, Process Optimization.
Scope of the Article: Big Data Analytics