Big Data & Marketing in Luxury
Context and goals
Our client is a global luxury group, grouping luxury houses in leather goods, fashion, watchmaking and jewelry. As part of its digital evolution and transformation, this client wanted to implement various Big Data actions in order to consolidate its data and improve its commercial performance.
The group called on Mind7 Consulting to support it in the implementation of a Big Data architecture which aims to:
- Consolidate all customer data to improve knowledge
- Have real-time access to customer sales information
- Transform and modernize its Information System
Processing large volumes of data
The big data project
Mind7 Consulting intervened to help control process performance on various business processes of the company. All the data entered by the counters of partner banks and agencies are centralized in a data warehouse.
The solution implemented by Mind7 Consulting made it possible to reconstruct the insurance processing processes and to detect by process mining the factors positively and negatively influencing the process performance (processing times, error cases, etc.). This makes it possible to concretely identify areas for improvement and good practices that can be generalized.
Assembled as a Feature team, the Mind7 Consulting consultants carried out the expected functionalities in Agile mode. A Product Owner and a Scrum Master from our client’s teams, but also many interlocutors from the business and IT production took part in the project, as well as teams in charge of “Front” and CRM functions. Thus Mind7 Consulting teams not only the Big Data architecture, but also all the applications and functionalities linked to the “Back-end” and on which the contribution of Real Time was most awaited.
A test and deployment phase in the field validated the proper functioning and usability of the solutions directly from the points of sale.
Effort and technologies
The technical environment is based on open source bricks such as Spark, Cassandra, Elastic, Docker, Nifi or Solr. Most of the developments are carried out in Scala. Monitoring was implemented with Jenkins and Git Lab CI.
The project was carried out in Agile mode, with iterations of 2 weeks and a total duration of 4 months.