Management framework for Big Data projects based on success factors and criteria - Souza e Silva, Karla de; Takagi, Nilton Hideki; Oliveira, Allan Gonçalves de (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI), 2025)
Context: The objective of this research is to examine success management in Big Data projects. Big Data informs strategic decisionmaking, but managing these projects effectively presents challenges to delivering value for organizations. Problem: The issue addressed is the lack of a structured management model focused on success in Big Data projects to reduce failure rates. Organizations face unique challenges with Big Data, often leading to project failures. Solution: Thiswork proposes a conceptual framework combining project management and success management for Big Data projects. It was developed through a literature review and interviews with experienced professionals in the field. IS Theory: This research is grounded in success management theory from project management, which is widely used in information systems projects. This study demonstrates its relevance to Big Data projects. Method: Design Science Research (DSR) methodology guided this study, including a literature review and expert interviews to refine a framework for Big Data project success management. Summary of Results: The framework addresses gaps found in the literature and interviews, covering project planning, execution, and closure, and incorporating the specific characteristics of Big Data and Success Management. Contributions and Impact on the IS Field: This research contributes to data science by applying success management principles, offering a practical and academic framework for Big Data project management. It provides strategies for effectively managing Big Data project success.