Design, implementation and production of a multi-cloud big data platform for a public transport company
Context
In the public transport sector, our mission was to design, build and put into production a multi-cloud Big Data platform. The challenges were numerous, including the organization of a complete overhaul of the decision-making system. The aim was to meet the challenges posed by the explosion in data volume, the heterogeneity of data formats, and the need to move closer to real time to better support operational teams. A crucial aspect of the project was also the development of data science to enable the prediction of ridership and journey times. The scope of intervention encompassed the fields of transport (all rolling stock), sales and marketing (customer knowledge).
Issues
Major challenges to overcome included the need to organize a complete overhaul of the business intelligence system to cope with the explosion in data volumes, the heterogeneity of data formats, and the growing demand for real-time data. The development of data science to predict ridership and journey times was also a strategic challenge. The scope of the project, covering the fields of transport, sales and marketing, required an integrated approach to respond effectively to these complex challenges.
Solution and method
To meet these challenges, our intervention took charge of several aspects of the project, including :
- Co-definition of the global strategy for data enhancement and governance.
- Design of the target architecture and its development, ensuring the transition from an existing "On Premise" platform to a "Hybrid-cloud / multi-cloud" platform.
- Proof of Concepts (Datavisualization, Data Science) to validate key concepts.
- Overall project management, ensuring effective coordination throughout implementation.
Benefits
The successful implementation of the new data platform has enabled the progressive roll-out of business packages. Benefits include advanced analytics capabilities, including ticket sales revenue analysis, benefiting all stakeholders involved. The results obtained offer a better understanding of the data, improved anticipation of ridership and journey times, enhancing operational efficiency and strategic decision-making in the public transport sector.