InnerSource for reproducible and extensible business analysis

Description

Data science and research communities are constantly evolving to incorporate new methodologies and tools to promote open, reproducible outputs. However, while business analysis has rapidly grown more data driven, the analyst community is slow to adapt to emerging best practices of reproducible and extensible workflows.

Emily Riederer maps common pain points in business analysis to established solutions from software engineering, data science, and the open science movement. Using a case study from Capital One, Emily discusses how designing empathetic, empowering, and engaging analytical tools is the key to igniting this organizational change and cultivating a sustainable InnerSource ecosystem.

Emily shares her experience building an opinionated R package to encapsulate common business analysis processes and encourage reproducible and extensible work. This required careful curation of tools, dependencies, and trade-offs to optimize over simplicity, efficiency, and transparency. Ultimately, it led to highly empathetic, user-driven design of intuitive, user-friendly workflows and a constantly evolving tool suite created entirely by business analysts.

Telling the parallel stories of package development and associate development, Emily outlines both design principles and key considerations for enterprise R packages and details best practices and lessons learned using the package to drive organizational change. You’ll also learn how Capital One demonstrated the value of this initiative to nontechnical stakeholders, upskilled associates with no coding background, and ultimately built an internal community of users and developers for a constantly growing suite of inner-sourced tools.