Overcoming scalability barriers in data science: A multidimensional approach

Abstract

We live in an era where data is being produced faster than most organizations can meaningfully process it. The sheer growth in data volume, variety, and generation speed has pushed conventional data science methods to their limits. This paper takes a close look at why scaling data science pipelines is so hard — not just from a technical standpoint, but also from an organizational and workflow perspective. We review current distributed computing approaches, identify where they fall short, and propose practical solutions backed by real-world case studies from healthcare, finance, and smart city contexts. Our findings suggest that no single technology or strategy can solve the scalability problem on its own; what’s needed is a coordinated effort that combines smarter algorithms, better infrastructure, and organizational change