BlaBlaCar matches drivers with empty seats with members who need a ride. With 10 million travelers connected each quarter—that’s 40 million a year—BlaBlaCar estimates that it has saved its riders more than £216 million. The Data Team at BlaBlaCar wanted to give employees across the company access to real self-service business intelligence.
To make this goal a reality, they chose the combination of Dataiku’s Data Science Studio for data prep in combination with Tableau for visual data analysis. They share completed visualizations through Tableau Server.
With help from Tableau and DSS, BlaBlaCar can quickly track how members are responding to new features or support R&D efforts like Machine Learning.
Tableau: Who uses the combination of Tableau and Data Science Studio (DSS) at BlaBlaCar?
Gaëlle Periat, BI Manager, BlaBlaCar: At BlaBlaCar, everyone uses Tableau or DSS. They have so many different uses: from approving a new feature, which means we're going to pull data from the application, to getting a visualization of: 'Does it work? Are people happy, etc.'
And they can also result in analyses with a huge impact on marketing or completely, more on R&D, which means we're going to try to do some testing in an even deeper, more probing way, going as far as machine learning, etc."
Thomas Cabrol, Chief Data Scientist, Daitaku: In terms of business use cases, there are all kinds of uses that are relevant for Blablacar with the combination of Data Science Studio + Tableau. The idea is that users can really manipulate and model their data in Data Science Studio, then visualize it, interact, and really analyze the data in Tableau.