Ten important roles for academic leaders in data science
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EDITORIAL
Open Access
Ten important roles for academic leaders in data science Jason H. Moore Correspondence: jhmoore@upenn. edu Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6116, USA
Abstract Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten important leadership roles for a successful academic data science chair, director, or dean. These roles include the visionary, executive, cheerleader, manager, enforcer, subordinate, educator, entrepreneur, mentor, and communicator. Examples specific to leadership in data science are given for each role.
Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten important leadership roles for a successful academic data science chair, director, or dean.
The visionary There is no question that successful data science leaders must have a compelling vision for the future. They must have a good understanding of data science and some ideas about where the field is going over the next 5 to 10 years. Part of developing an exciting vision is knowing where the field is now and being able to extrapolate from current trends. This might be enough to develop a solid five-year vision. However, it is difficult to see 10 years into the future for a field developing and changing so rapidly. This requires synthesis and imagination. It requires asking questions about what could happen and making judgements about the probability of occurrence. Where will highperformance and cloud computing be in 10 years? What will programming languages look like in 10 years? Will artificial intelligence still be hot in 10 years? If so, what will it look like? Will we have solved all the data cleaning and integration issues we struggle with so much today? Where are the fields of statistics and applied mathematics going? Will software finally be user-friendly? What will be the important basic science, clinical, and public health questions? How much will data continue to grow in volume and complexity? What will data science education and training programs need to look like © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the art
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