Big Data and Strategic Decision-Making: Towards a Deep Learning-Based Approach

Abstract

The explosion in the volume of digital data generated by organisations, social media, connected devices and information systems has profoundly transformed decision-making processes. Big Data is now a strategic resource enabling businesses and institutions to develop more effective and responsive decision-support systems. However, the complexity, diversity and velocity of the data require the use of advanced analytical techniques capable of efficiently extracting relevant insights from vast datasets.

In this context, deep learning – an advanced branch of artificial intelligence based on deep neural networks – is emerging as a key technology for harnessing big data. According to Hou et al. (2024), deep learning models offer remarkable capabilities for machine learning and the identification of complex patterns, enabling significant improvements in the performance of decision-making systems. Similarly, Kokol (2024) emphasises that the integration of artificial intelligence into analytical processes helps to increase the accuracy of predictions and optimise decision-making across various sectors.

This article analyses the interactions between big data and the decision-making process, focusing on the contributions of deep learning to decision intelligence. It presents the main architectures used, notably artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Transformer models, as well as their applications in the fields of healthcare, finance, telecommunications, e-commerce and public administration. The study also examines the challenges relating to data quality, information governance, data security and the computational requirements associated with deep learning models.

The analyses show that integrating deep learning into big data environments can significantly improve the speed, reliability and relevance of strategic and operational decisions. However, several challenges remain, particularly regarding model interpretability, the protection of personal data and the availability of suitable technological infrastructure. Deep learning therefore emerges as a key driver of transformation in modern decision-making systems and offers a promising avenue for the development of advanced decision intelligence in the digital age.

Keywords: Big Data; Decision-making process; Deep Learning; Artificial intelligence; Predictive analytics; Decision support; Neural networks; Decision intelligence; Data science; Digital transformation.

Received Date: April 21, 2026

Accepted Date: May 12, 2026

Published Date: June 01, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/734