Abstract

Author:
Ganiyu Bolawale Omotoye, Oluwaseun Peter Oyeyemi, Binaebi Gloria Bello, Azeez Jason Kess -Momoh, Andrew Ifesinachi Daraojimba

Doi: 10.26480/seps.01.2024.52.59

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

This study provides a comprehensive review of the impact of augmented analytics (AA) on business decision-making in the U.S., focusing on its integration within business intelligence and implications for various stakeholders. The primary objective is to understand how AA revolutionizes traditional business intelligence by integrating AI and machine learning, thereby enhancing efficiency and decision-making capabilities in various business sectors. Employing a systematic literature review and content analysis, the study examines peer-reviewed journals, conference proceedings, official reports, and case studies from 2013 to 2023. The methodology includes a detailed search strategy, stringent inclusion and exclusion criteria, and a robust selection process to ensure a comprehensive understanding of AA’s role in business. Key findings reveal that AA significantly influences business efficiency, competitive advantage, and stakeholder dynamics. It has transformed traditional business processes by offering predictive analytics and improved data visualization. However, the expansion of AA also presents challenges such as data privacy concerns, the need for continuous skill development, and potential algorithmic bias. The study concludes that AA is a pivotal element in modern business operations, necessitating a balance between technological innovation and ethical considerations. Strategic recommendations are provided for business executives, IT experts, and researchers, emphasizing the importance of fostering a data-driven culture, investing in AA technologies, and exploring new methodologies and tools. Future research directions include examining the long-term impacts of AA on business strategy, developing ethical frameworks for AI and analytics, and exploring new applications in various industries.