Le Pain Quotidien - A data and AI roadmap for international growth
Le Pain Quotidien needed a shared data direction across around 230 company-owned and franchise locations. We assessed the existing landscape and translated its business priorities into three architecture scenarios and phased data and AI roadmap, giving leadership clear options for its next investment decision.
xplus, part of Yuma, helped Le Pain Quotidien define how data can support its business over the next three to five years, from stronger franchise reporting and customer insights to future AI applications.
Le Pain Quotidien is an international restaurant and bakery group with around 230 company-owned and franchise locations in approximately 20 countries.
Its international structure also creates complexity. Business information sits across different systems and operations, while future plans require data to become more accessible and consistently governed. Le Pain Quotidien wanted to establish what that should look like and how to move towards it in realistic steps.
The business challenge
Bringing fragmented business data together
Point-of-sale, ERP, CRM, marketing and franchise systems all contribute valuable information, but that information was distributed across the organization. The use of different tools made it difficult to combine sales, finance, marketing and operational data.
Franchise sales reporting was slow and relied heavily on Excel files collected manually from different countries and franchisees. Collecting this information required considerable manual effort, while limited standardization contributed to data quality issues.
Before investing in a future data platform, Le Pain Quotidien needed a clear view of what it already had, where the main gaps and priorities were and how the needs of corporate and franchise operations could be accommodated within one approach.
The ambition: Creating the foundation for wider use of data
Better franchise reporting, richer customer insights and greater operational efficiency were among the priorities. Analytics and AI would create further opportunities over time.
This required more than a technology choice. We needed to establish the architecture, governance and platform capabilities that could support these ambitions as they developed.
The approach
1. Assessing the current data and reporting environment
We started by mapping the existing business environment. Interviews with business and IT leaders surfaced strategic priorities, operational needs and pain points. Our assessment of the application landscape showed how information currently moved between point-of-sale, ERP, CRM, marketing and franchise systems.
Close collaboration between business and IT was central throughout the engagement. It helped us test assumptions early, balance operational realities with longer-term ambitions, and ensure the resulting direction was workable across both corporate and franchise operations.
2. Designing the target architecture and governance model
With that foundation in place, our experts developed the enterprise data architecture blueprint, target data platform architecture and data governance framework.
Rather than recommending one predefined solution, we developed three alternative target architectures. Each came with its own investment scenario, allowing executive management to compare different routes forward and understand the implications of each.
3. Prioritizing capabilities and use cases
The next step was to determine which capabilities would deliver the most value and when they should be introduced.
We assessed potential analytics and AI use cases, including opportunities to improve operational performance and customer engagement. These were translated into a prioritized roadmap for the next three to five years, with implementation phases, business cases and investment options.
An executive presentation brought the findings together into clear recommendations for decision-making, providing a realistic path from strategic direction to phased
The outcome: Turning strategy into clear investment choices
Le Pain Quotidien's executive management now has three concrete architecture and investment scenarios to evaluate, each showing a different route toward the target data environment.
Behind those choices sits a phased roadmap that connects business priorities to the required data capabilities, governance and technology investments. This gives Le Pain Quotidien a practical basis for deciding what to invest in, in what order and over what timeframe.
Data from different source systems is transformed into reliable, standardized, and meaningful information for business, analytics, and AI. Quality is monitored end-to-end, from technical data health and content quality to business performance.
