Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform
Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform


The space between business and engineering functions in a large enterprise creates room for inefficiencies to grow. While large-scale corporate engineering is best left to the professionals, the traditional requirement-submission and solution-delivery process doesn’t meet the needs of the new analytic speed and scale required in highly dynamic and diverse organizations. A better approach is to empower those closest to the analytics work: business analysts with formidable knowledge of the domain. Python's continued adoption and support, combined with easy-access platforms like Jupyter, provides a powerful opportunity to put analytic development and data science capability directly in the hands of the business analysts. Technical solutions alone, however, aren’t sufficient. Organizations also need to develop the right outreach, training, and support mechanisms to properly pair technical advances with the operational needs of their workforces.

This case study from inside the US Intelligence Community (“IC”) details how Jupyter has empowered, and continues to empower, thousands of business analysts to create their own analytic solutions, saving them time and streamlining their workflow. Four years of concerted effort by a small team to evangelize Python and Jupyter within a large enterprise setting have netted tremendous gains. Through the right combination of outreach and training, alongside platform enhancements and internal evangelism, business analysts are finally finding themselves as a community on the same side of the wall as solutions development. But the story doesn't end there – a DIY analytics movement introduces new challenges, including an abundance and redundancy of solutions. This movement would fail under its own weight without significant efforts to manage, curate, sustain, and provide a corporate “path to production” for the hardest-hitting new capabilities. Our talk will describe this path to Jupyter adoption from the vantage point of the enabling team, the breadth of challenges we faced (both anticipated and unanticipated), and how we overcame those challenges to transform business analysis in the IC.


Dave Stuart is a senior technical executive within the US Department of Defense, where he’s leading a large-scale effort to transform the workflows of thousands of enterprise business analysts through Jupyter and Python adoption, making tradecraft more efficient, sharable, and repeatable. Previously, Dave led multiple grassroots technology adoption efforts, developing innovative training methods that tangibly increased the technical proficiency of a large noncoding enterprise workforce.

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