Published In
IEEE Engineering Management Review
Document Type
Pre-Print
Publication Date
12-1-2024
Subjects
Data Sources -- Analytics
Abstract
The implementation of modern Business Intelligence (BI) 4.0 and Augmented Analytics solutions is drastically influenced by organizational factors, which are widely recognized as significant drivers of success. Many researchers endeavor to understand critical success factors of BI implementation using various models, most all of which include Organizational components, along with others. Earlier studies have suggested components within the Organizational perspective play a critical role in driving overall BI implementation success. However, the research on organizational factors that focus on the advanced version of BI and augmented analytics is not well established. Addressing this need, the current work focuses on filling the gap by focusing on organizational factors within the context of BI 4.0 and AI-driven augmented analytics. The absence of such perspective compromises a thorough evaluation of factors that are influential in effective adaptation and absorbance of modern BI solutions. The following research explores drivers of BI implementation success from the Organizational perspective, including widely accepted notions of management support, organizational culture, organizational structure, and adequate resource availability. Additional sub-components contributing to the outcome of implementation efforts such as organizational agility, the presence of a hub and spoke analytics function, existence of a data-driven culture, executive sponsorship, and team skills are included, among others.
Rights
© Copyright the author(s) 2024
Locate the Document
DOI
10.1109/EMR.2024.3506865
Persistent Identifier
https://archives.pdx.edu/ds/psu/42880
Publisher
IEEE
Citation Details
Williams, R. A., Duman, G. M., Kongar, E., & Sheikh, N. J. (2024). Organizational Factors Enabling Augmented Analytic and BI 4.0 Implementation Success. IEEE Engineering Management Review, 1–23. https://doi.org/10.1109/emr.2024.3506865
Description
This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and content may change prior to final publication.