Exploring the impact of big data on companies' business intelligence strategies in the digital era

Mohammad Musa Al-Momani () and Israa Musa Al-Momani ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 5, 883-891

Abstract: Using a mixed-method approach, this research examines how big data affects business intelligence strategies in the digital age. It points out the importance of the big data analytic process in decision-making, strategic planning, and identifying growth opportunities. Nevertheless, incorporating big data into business intelligence strategies has challenges: complex data integration, privacy concerns, and the availability of skilled analysts. Nonetheless, its potential to drive innovation and growth is non-negotiable. The study findings contribute valuable insights to organizations looking to effectively navigate the embrace of big data and thrive in a data-rich digital age as more businesses embrace data-driven decision-making, the implications of this research extend beyond individual organizations This study adds to existing knowledge in the field by shedding light on the evolving landscape of big data in business science approaches that shape industry, both economic and societal. Organizations are recommended to take the practical advice of this survey to gain the full benefits from big data and unleash its transformative power. As the digital age evolves, businesses must embrace big data to remain competitive and flexible amid dynamic market forces.

Keywords: Big data; Business intelligence; Data-driven; Decision-making; Digital era; Predictive analytics. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/1791/630 (application/pdf)

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:ajp:edwast:v:8:y:2024:i:5:p:883-891:id:1791

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().