Retail: data architecture consulting
Context
For a major retailer in French-speaking Switzerland, the challenge was to help our client define and implement a data architecture.
The customer's IT environment is composed of several internally developed applications, which have evolved considerably and are becoming increasingly complex. These applications are reaching their limits in terms of scalability and maintenance.
Challenges
Our customer does not currently have an integration tool to mediate between the various tools in its IT ecosystem. For the time being, the integration principles are point-to-point. Faced with these challenges, a team was set up at the beginning of 2022 to define the data architecture requirements and initiate data centralization. The major challenge is to overcome the current constraints, both in terms of scalability and maintenance, by moving to a more flexible solution.
Solution and method
Our work focuses on requirements scoping, analysis, creation of use cases and establishment of acceptance criteria. The goal is to implement a flexible solution capable of responding to current challenges and keeping pace with emerging trends at our client's facilities. To achieve this, a series of workshops were organized in which data challenges and pain points were collected. The main steps were data mapping (where the data is coming from), an in-depth review of the data warehouse (data cleansing process) and connecting with third parties to open up the data to ecosystems.
Benefits
The expected benefits of this transformation are manifold. The goal is to ensure that databases can effectively and sustainably support ongoing transformation projects. The implementation of comprehensive recommendations for building a complete data architecture (ingest, transformation, storage, modeling and restitution) is intended to ensure a clear roadmap for transformation, including deployment iterations. Ultimately, the benefits will translate into better support for our clients' strategic challenges by aligning the paths needed to build an optimal data environment.