Using OLAP Cubes as Dataset for Neural Networks: Integrating Business Intelligence and Artificial Intelligence

The integration of OLAP cubes and neural networks represents a significant advancement in business intelligence. OLAP cubes, with their multidimensional data structures, enable efficient analysis across multiple dimensions, helping businesses extract insights from their data. Neural networks excel at learning from large datasets and making accurate predictions. This paper explores how OLAP cubes can be used as datasets for training neural networks. The process includes extracting and preprocessing data to make it suitable for neural network training. The practical applications of this integration in business include improved forecasting, optimization, and strategic decision-making. By combining the analytical capabilities of OLAP cubes with the predictive strengths of neural networks, businesses can achieve more precise and actionable insights. Furthermore, the paper discusses future development and research possibilities in this area, emphasizing the potential for creating better business intelligence solutions. This integration opens new possibilities for enhancing data analysis, making it a promising area for future exploration.

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