Empowering Data Science Teams: How Automation Frameworks Address Competency Gaps Across Project Lifecycles

In the fast-evolving field of data science, the combination of the right team competencies has a major impact on a successful project execution. These competencies, ranging from data acquisition to model deployment, are increasingly difficult to maintain due to widespread competency shortages. This puts data science projects at risk of delays, inefficiencies, and failure, as organizations struggle to find skilled professionals. Automation frameworks - software tools designed to automate repetitive or complex tasks - offer a solution to this challenge. While these frameworks provide benefits such as reducing manual labor and improving project efficiency, they have notable limitations, particularly in covering critical phases like business understanding and deployment. Additionally, training programs also struggle to fully address the competency gap due to time, cost and scalability constraints. This paper investigates how existing automation frameworks can fill these competency gaps within data science teams more effectively. Using the CRISP-DM model as an example of a structured process, this study first identifies tasks required in each phase. Then, it matches these tasks with relevant automation frameworks to assess the extent of automation possible. Finally, these tasks are mapped to the EDISON Data Science Competence Framework to highlight which competencies automation frameworks can address. The findings suggest that automation frameworks effectively bridge competency gaps, enabling teams to complete projects more efficiently and effectively where human expertise may be lacking. In this manner, our findings serve as a reference point for data scientists and practitioners alike.

I. Introduction

In today’s digitized world, data serves as a key resource that profoundly influences economic decisions. These decisions guide strategic actions across industries, as companies collect and analyze large volumes of data to gain insights, drive decision-making, and secure competitive advantages [54]. Despite these efforts, many companies struggle to succeed in data science projects [23]. According to [64], [p.1], "87% of these projects fail to make it to production". A structured and systematic approach to project implementation is essential for reducing risks and improving success rates [24], [31], [54].