Leadership in Data Science¶
Overview¶
On one hand, data science is broadly and inclusively considered as any research driven by or conducted using data. On the other hand, leadership is defined as specific skills “encompassing the ability of an individual or organization to lead or guide other individuals, teams, or entire organizations.” (source: Wikipedia).
When examined closely, leadership in research is an important, but challenging concept to pinpoint and often discussed in terms of leadership skills. This is reflected by the many theoretical frameworks, research schools, and books that have tried to explain, define, or assess leadership skills. However, it is also widely considered that there is no such thing as a fixed set of leadership skills as not all great leaders share the same traits or strengths [HayesLeadership].
Fig. 14 height: 500px name: leadership-wordcloud alt: Word cloud from Wikipedia article about leadership¶
Words that stand out in a word cloud from the Wikipedia entry about leadership.
In the context of data science, where we thrive to advance our knowledge through validated scientific methods based on data, talking about leadership, a fuzzy concept, is not only challenging, but also a rare finding. Therefore, a chapter about data science leadership is required in The Turing Way. Afterall, “technical skills are just one aspect of making data science research open for all” (ref).
Leadership is considered one of the most important aspect of data science, but it is also:
one of the hardest non-technical skills to learn without guidance,
rarely discussed openly in academic research environments, in spite of being everywhere, and
healthy leadership is one of the keys to healthy and inclusive communities.
We hope that by writing about leadership, we will:
put the spotlight on a fuzzy but powerful construct,
inspire those who think leadership is not for them to review their assumptions (and maybe see themselves with abilities to lead a project), and
ignite reflection and spark conversations about the leaderships we all come across in data science,
evaluate if leadership in our workplace or communities are healthy, compasionate, and inclusive, and how they could be improved.
In this chapter, we will introduce and discuss (somewhat in an unorderly manner):
important features of leadership when experiencing and exercising them in data science.
what we mean by “healthy” leadership and how we can build them.
why structure is leadership matters and how to build it
how to bring diversity in leadership
how we can create leadership opportunities in our projects
what critical aspects should be avoided in leadership
finally, with the help of stories of leadership shared by different members, the last part of this chapter will highlight how and where one can find leadership opportunities in data science, see the Personal Stories subchapter.