Loading…
This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Thursday, July 6 • 5:55pm - 6:00pm
Statistics hitting the business front line

Sign up or log in to save this to your schedule and see who's attending!

Feedback form is now closed.
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.


Speakers
AL

Anne Lund Christophersen

Senior Specialist, Vestas Wind Systems A/S



Thursday July 6, 2017 5:55pm - 6:00pm
4.02 Wild Gallery

Attendees (102)