Data science image via Shutterstock
By James Kobielus (@jameskobielus)
When you think of teams, the word “chemistry” is always lurking in your thoughts. Chemistry is what distinguishes top-performing teams from the dysfunctional ones.
Personalities are one thing, and productivity is quite another. Where data scientist teams are concerned, you are quite likely to find one dominant personality type: people who have ample curiosity, intellectual agility, statistical fluency, analytical acuity, research stamina, and scientific discipline.
However, where productivity is concerned, data scientists vary widely. Some work best in blissful isolation whereas others thrive in collaborative environments. Likewise, some data scientists are awesome polymaths who have mastered a wide range of skills, while others are strict specialists. Some are closer to the statistical analyst end of the skills spectrum, whereas others take pride in being the subject-matter expert that all the data scientists runs to when the question turns to marketing, finance, and what have you.
The productivity of the entire data-scientist team depends on being able to balance this mix of people, aptitudes, skills, and roles. It also depends on being able to incorporate new roles into the team as the nature of big data and data science initiatives evolves. For example, the notion of a “customer experience modeler” is of fairly recent vintage. It may be someone with a degree in the humanities, not mathematics and statistics.
The blend of skills in data-science teams is changing, and the chemistry among new and established disciplines will grow trickier as this trend intensifies. This new reality is the focus of a recent InformationWeek article, “How To Build An Analytics A-Team.” The piece discusses a study by Blue Hill Research in which that firm outlines several important roles within data-science organizations:
• Data visualizer: visual orientation, focusing on innovative ways of presenting data-driven insights within “instinctual graphics”
• Data custodian: quality orientation, focusing on data cleansing and master data management
• Data evangelist: application orientation, focusing on identifying new uses for big data analytics
• Contextual analyst: narrative orientation, focusing on interpreting quantitative insights within the larger business context
• Neuro-analyst: cognition orientation, focusing on how humans can best interact with data-driven analytics to drive comprehension and exploration
If you’ve already included all or many of these as distinct jobs in your initiative, you should consider creating a “center of excellence” supervisor whose job it is to build up the environment where cross-role chemistry takes hold.