Keywords: enterprise, collaboration, business,
rmarkdown.
We will introduce you to a framework we developed to achieve effective collaboration around data analysis in our enterprise environment at Vestas. In this talk we will describe our implementation in
R, why we chose
R, which challenges we faced and what we learned during the process.
Setting the scene We had the task of creating statistical models to be used by the sales teams. Sales already had an
Excel based tool, and the requirement was that we should continue with this front end. The statistical work would require models developed by a team of people as well as involvement of subject matter experts, hence the framework needed to support collaboration.
On stage
- Sales (end users, 50-100 people around the globe)
- Data analysts and subject matter experts (project team, 10 people in DK + IN)
- In front: Existing Excel front end
- In the background: R, GIT, rmarkdown, SQL
Scenography Being in an enterprise world we had to fulfill requirements for maintainability, documentation and reproducibility. At the same time we wanted to achieve i) a code base approach, ii) easier validation methods, iii) automated model deployment and iv) a strong collaborative platform.
Orchestration On the technical side the main new feature is a self-made package called
harvester. The
harvester’s main functions allow us to run markdown-files and fetch selected objects, typically our statistical models.
These fetched models are then wrapped into another internal package called
models together with interface functions. This is the package used by Sales. The
models are made available to
Excel through a self-developed
.NET-wrapper. In this way the end users will be able to get the most recent models through their normal
Excel tool.
The collaboration is done through
GIT where all team members store their analysis
R-markdowns, shared and validated by subject matter experts. The
harvester is designed to run the markdowns in
GIT and fetch the selected output models.
Review Get inspired on how to integrate validated statistical models into the decision making in the business front line: It is a five star movie.
