An alternative approach to adding data science to an organization: Use Jupyter and start with the domain experts

Description

As an emerging trend that capitalizes on the most significant new breakthroughs in technological computing, data science promises to bring enormous value to enterprise organizations. However, attempts to fill this role meet a shortage of talent. As a result, many data science vacancy ads go unfilled. But data science is not an exclusive skill set. It encompasses a variety of skills and tools, many of which are approachable to a wide range of practitioners. While at the high end, the race to acquire in-depth subject-matter expertise on activities like deep learning and AI can be intense, it’s easy for organizations to overlook the ability to transition their current workforce, steeped in domain knowledge, into roles of beginner and intermediate data science practitioners.

This case study from inside the US intelligence community describes an effort to empower those closest to the analytic challenges: business analysts whose formidable knowledge of the domain have proven to be equally if not more valuable as a background in advanced statistics or computing. Dave Stuart explores the high mission impact obtained through wide-scale, community-based adoption of the Jupyter Notebook supported by the creation of an enterprise Jupyter Notebook sharing and collaboration platform. By taking into account the specific needs of domain knowledge experts who are just beginning their forays into lightweight data science, organizations can see a large return on their investment.