Redefining Business Intelligence Architecture with the EDAS Optimization Model

Business Intelligence (BI) architecture serves as the backbone for data-driven decision-making by integrating various tools, processes, and methodologies to transform raw data into meaningful insights. This study explores an enhanced BI architecture, evaluating its efficiency using EDAS (Evaluation Based on Distance from Average Solution) is a method for evaluating alternatives by measuring their distance from an average or ideal solution. The approach calculates the differences between the alternatives and the average solution to assess their relative performance. This technique is often used in decision-making processes where multiple options need to be compared based on various criteria. Comparing it with alternative circular models. The study aims to assess BI architecture’s effectiveness in terms of user safety, adaptability, cost, and time. Research Significance: The growing complexity of data management and the increasing demand for real-time analytics necessitate a robust BI architecture. This research identifies critical factors influencing BI performance and provides a comparative analysis of alternative models. The findings contribute to optimizing BI solutions for various industries, ensuring scalable and secure decision-making frameworks. Methodology: EDAS The EDAS method is employed to evaluate BI architecture by analyzing its deviation from an ideal solution. It quantifies performance based on predefined evaluation parameters, ensuring an objective assessment. Alternative Models: Circular 1 – Traditional BI model with structured data processing. Circular 2 – A cloud-based BI framework focusing on scalability. Circular 3 – A hybrid BI approach integrating AI-driven analytics. Circular 4 – A decentralized BI model for real-time, distributed data handling. Evaluation Parameters: The BI architecture is assessed based on the following factors: Users’ Safety – Ensuring data security, privacy, and compliance. Adaptability to Changes – Ability to integrate new data sources and adjust to evolving business needs. Cost – Budget efficiency concerning implementation, maintenance, and scalability. Time – Speed of data processing, reporting, and overall system performance. Results: The study offers a comparison of the different BI models, identifying the most efficient architecture based on the evaluation parameters. The findings highlight the optimal balance between security, adaptability, cost-effectiveness, and time efficiency in BI implementations.