Data architecture consulting for a leading Swiss retailer
Context
For a major retailer in French-speaking Switzerland, the challenge was to help our customer define and implement a data architecture.
The customer's IT landscape is made up of various in-house developed applications, which have evolved considerably and are becoming increasingly complex. These applications are reaching their limits in terms of scalability and maintenance.
Issues
At present, our customer does not have an integration tool to mediate between the various tools within its IT ecosystem. For the time being, integration principles are of the point-to-point type. Faced with these challenges, a team was set up in early 2022 to define data architecture requirements and initiate data centralization. The major challenge is to overcome current limitations, both in terms of scalability and maintenance, by establishing a transition to a more flexible solution.
Solution and method
Our intervention focuses on scoping requirements, analysis, building use cases, and establishing acceptance criteria. The aim is to implement a flexible solution capable of meeting current challenges and keeping pace with emerging trends at our customer's site. To achieve this, a series of workshops was organized to gather data challenges and pain points. Key steps included data mapping (data origin), an in-depth review of the data warehouse (data cleansing process), and connecting to third parties to open up data to ecosystems.
Benefits
The expected benefits of this transformation are manifold. The aim is to ensure that data foundations can effectively and sustainably support ongoing transformation projects. The implementation of global recommendations for building a complete data architecture (ingestion, transformation, storage, modeling and restitution) aims to guarantee a clear roadmap for transformation, including deployment iterations. Ultimately, the benefits will translate into better support for our customer's strategic challenges, by aligning the trajectories needed to build an optimal data environment.