Integration of Data Science Projects in Enterprise Architecture Modeling

Abstract

Data Science (DS) technologies provide crucial competitive advantages in contemporary business environments through optimized processes. However, DS-based service systems require collaborative ef�forts between IT and business divisions, necessitating structured frame�works to bridge organizational gaps and ensure alignment between tech�nical requirements and business objectives. This research examines the integration of DS systems within Enterprise Architecture (EA) models to address complex implementation challenges. EA modeling provides an in�dispensable, structured framework for ensuring the optimal performance of DS systems, particularly in the context of digital transformation ini�tiatives, and enables the seamless integration of data science capabilities into organizational frameworks. This work presents a thorough litera�ture review that critically examines existing research on integrating DS projects into EA models, offering a comprehensive view of DS integra�tion to enhance architectural frameworks. Future work will demonstrate these holistic models in practical enterprise applications and conduct case studies to refine the model and evaluate the effectiveness of conceptual modeling approaches