Ten important roles for academic leaders in data science

Area of Science:

• Data science
• Biomedical data mining
• Big data analytics

Background:

• The proliferation of big data and advancements in biological and biomedical data mining have spurred the growth of data science as a critical academic discipline.
• This growth is evidenced by the increasing number of dedicated data science departments, research centers, and educational programs worldwide.

Discussion:

• Effective leadership in academic data science necessitates a multifaceted approach, encompassing ten distinct roles.
• These roles include strategic vision (visionary), operational oversight (executive, manager), team motivation (cheerleader), adherence to standards (enforcer), collaborative integration (subordinate), knowledge dissemination (educator), innovation (entrepreneur), talent development (mentor), and effective information exchange (communicator).

Key Insights:

• Successful academic data science programs depend on leaders who can embody diverse leadership functions.
• The identified roles provide a framework for understanding the complex demands placed upon data science chairs, directors, and deans.
• Specific examples illustrate the practical application of these roles within the data science context.

Outlook:

• Developing leaders adept in these ten roles is essential for the continued advancement and integration of data science in academia.
• Future data science initiatives will benefit from leaders who can balance strategic, operational, and interpersonal responsibilities.
• This leadership model can guide the establishment and enhancement of data science education and research globally.