Beyond Unicorns: Educating, Classifying, and Certifying Business Data Scientists

Thomas H Davenport

doi:10.1162/99608f92.55546b4a

Abstract

There is increasing recognition that the data scientist âunicornââone who can master all the necessary skills of data science required by businessesâexists only rarely, if at all. Successful data science teams in business organizations, then, need to assemble people with a variety of different skills. This is only possible at scale with clear classification and certification of skills. While such certifications and classifications are in their early days, some firms are beginning to create them, and they are beginning to emerge in professional associations as well. Ideally, universities and other education providers and certifiers of data science skills would also employ standard skill classifications to communicate the skills they intend to inculcate.

Highlights

There is increasing recognition that the data scientist ‘unicorn’—one who can master all the necessary skills of data science required by businesses—exists only rarely, if at all
Successful data science teams in business organizations, need to assemble people with a variety of different skills. This is only possible at scale with clear classification and certification of skills
While such certifications and classifications are in their early days, some firms are beginning to create them, and they are beginning to emerge in professional associations as well

Summary

Data Science Unicorns Really Don’t Exist

Data science is a new and popular, but difficult-to-define field. Its true age (Donoho, 2017), its relationship to previously existing fields like statistics (Gelman, 2013), and the nature of its ‘true’ practitioners (Gutierrez, 2019) are widely discussed and debated. Since it is probably safe to say that academics care more about clear definitions of disciplines and terms than do businesspeople, there may be even less clarity about what constitutes data science and data scientists in the business domain It is defined, data science is increasingly a mission-critical activity for businesses and organizations, and one involving a variety of tasks and skills. Patil (who says he co-suggested the first use of the data scientist term for a business role at LinkedIn in 2008), I found that the most common academic background was experimental physics, but there were data scientists with backgrounds in astrophysics, statistics, sociology, meteorology, artificial intelligence, and many others (Davenport & Patil, 2012) They performed a variety of different types of tasks at that time as well. Companies—and ideally the entire society—need to develop certification and classification structures that make visible and reliable the different types of skills that data scientists possess

Business Data Science Teams Require Multiple Skillsets

Enterprise Data Science Job Role and Skill Structures

The Value of Enterprise Job Role and Skill Structures

Findings

Classifying and Certifying Data Science Skills