What's in your workflow? A presentation by Emily Riederer (emily.riederer@capitalone.com), a Sr. Analyst for Capital One from Data Pop Up Chicago in November of 2017.
While business analysis rapidly grows more data-driven, the analyst community is slow to adapt the best practices of data science workflows. Many parallels exists between data science “top topics” (e.g. reproducibility) and business pain points, but these common needs are obscured by the different “languages” of these two communities. The opportunity cost is greatest in heavily regulated industries such as finance and insurance where documentation and compliance are paramount.
In this talk, we will review our experience transitioning Capital One business analysts from legacy systems to open-source workflows by developing user-friendly tools. We incentivized business analysts to adopt the data science mindset by curating open-source tools and developing code packages which simplify workflows and eliminate pain points.
Our internal R package, tidycf, reimagines cumbersome Excel cashflow statements as dataframes and uses RMarkdown templates and the RStudio IDE for an intuitive, user-friendly experience without the overhead of maintaining a custom GUI. We tackle challenges in documentation and communication while immersing new users in the R language.
We will share best practices and lessons learned from our experience designing tools for non-technical end-users, standardizing workflows based on the RStudio IDE’s infrastructure, and evangelizing data science methods.
Check out her slide deck for this presentation here.
