Agile Methodologies for Managing Complexity in Machine Learning and Big Data Projects for Business Markets


  • Justin Scott, Jack Nathan Department of Computer Science, Tulane State University


Agile methodologies, Machine learning, Big data, Project management, Complexity, Business markets


As businesses increasingly rely on machine learning (ML) and big data analytics to drive decision-making and innovation, the management of complex projects in these domains becomes paramount. Agile methodologies offer a flexible and iterative approach to project management that is well-suited to the dynamic and uncertain nature of ML and big data projects. This paper explores the application of agile methodologies for managing complexity in ML and big data projects within business markets. Drawing on empirical research and industry case studies, we examine the challenges of traditional project management approaches and highlight the benefits of agile methodologies in fostering collaboration, adaptability, and value delivery. By integrating agile principles and practices into project management processes, organizations can enhance project success, accelerate time-to-market, and capitalize on the transformative potential of ML and big data analytics.