Artificial neural networks in the development of business analytics projects

Juan Bernardo Quintero, David Villanueva-Valdes and Bell Manrique-Losada

International Journal of Information and Decision Sciences, 2024, vol. 16, issue 1, 46-72

Abstract: The accelerated evolution of information and communication technologies, with an ever-growing increase in their access and availability, has become the foundation for the current big data age. Business analytics (BAs) has helped different organisations leverage the large volumes of information available today. In fact, artificial neural networks (ANNs) provide deep data mining facilities to organisations for identifying patterns, predict probable future states, and fully benefit from predictions/forecasts. This article describes three ANNs application scenarios for the development of BA projects, by using network learning for: 1) executing accounting processes; 2) time series forecasts; 3) regression-based predictions. We validate scenarios by implementing an application-case using actual data, thus demonstrating the full extent of the capabilities of this technique. The main findings exhibit the expressive power of the programming languages used in data analytics, the wide range of tools/techniques available, and the impact these factors may have on the BA development projects.

Keywords: artificial neural networks; ANNs; business analytics; data analytics; big data; deep data mining; network learning process; time series forecast; regression-based prediction; activity-based costing; supervised learning; decision making. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=136283 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:16:y:2024:i:1:p:46-72

Access Statistics for this article

More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().