A Hybrid Business Intelligence and Knowledge-Based Framework for Organizational Decision Support

Hlaing Htake Khaung Tin, Samun Karki, Amit Rai, Anuj Karki, Mohamed Ahmed Abdelwareth Mahmoud Ahmed, Mohab Tamer Salah El Din Saad Salah Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Department of Information Technology, University of Sunshine Coast, Sippy Downs, Queensland, Australia. Department of Computer Science Engineering, Lancaster University, Bailrigg, Lancaster, Lancashire, United Kingdom. Department of Information Technology, Guangxi University, Nanning, Guangxi, China. Department of Biomedical Sciences, Zewail City of Science and Technology, 6th of October City, Giza Governorate, Egypt.

Abstract: Organizations use data-driven decision-making to enhance their operational efficiency, strategic planning and competitive performance. Nevertheless, traditional BI systems primarily focus on acquiring, processing and visualizing structured data, without providing sufficient opportunities to integrate organizational knowledge, business rules and contextual reasoning. In this paper, the authors propose a hybrid Business Intelligence and knowledge-based framework for organizational decision support that unites data analytics and knowledge representation with rule-based reasoning. The proposed framework includes data sources, a business intelligence (BI) layer, a knowledge base, a rule base, an inference engine, and a decision-support interface that collectively convert organisational data and knowledge into meaningful recommendations. The framework allows decision-makers to analyse historical and current organisational data using BI tools while incorporating organisational norms, policies and expert knowledge into the decision-making process. The inference engine uses available information and applies appropriate rules and knowledge to produce logical, understandable recommendations. These recommendations are then presented to the decision-support interface in a clear, understandable way, so that managers can grasp the rationale behind each decision. Overall, the framework provides a systematic approach to integrating data-driven analytical insights and knowledge-driven reasoning, thereby enhancing the quality, transparency, consistency, and reliability of managerial decisions across organisations.

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Keywords: Business Intelligence; Knowledge-Based Systems; Decision Support; Knowledge Representation; Rule-Based Reasoning; Data Analytics; Organizational Decision-Making.

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Received on: 02/07/2025Revised on: 25/08/2025Accepted on: 04/11/2025Published on: 05/03/2026

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