The Challenge: Retail Scale Meets Data Complexity 

Colruyt Group operates at a level of data intensity that few retailers experience. Every day, more than 100 million point-of-sale transactions are generated across stores, resulting in billions of records flowing into analytical systems. In some cases, individual Data Vault satellites exceeded 8 billion records. 

At the same time, data was spread across CRM, ERP, and numerous operational platforms owned by different subsidiaries. Forecasting relied on manual processes and lacked the store-level and product-level detail needed for fast, confident decisions. Differences in terminology and ownership between IT and business teams created friction around data models and taxonomy. 

As data volumes continued to grow, this operating model began to show its limits. Manual forecasting could not keep pace with daily retail decisions, fragmented data slowed response times, and inconsistencies across domains made it harder to act with confidence. At Colruyt’s scale, continuing this way would have meant slower innovation, higher operational risk, and missed opportunities on the shop floor. 

The Approach: A Metadata-Driven Data Operating Model 

Colruyt Group selected VaultSpeed to fundamentally change how data products are designed, delivered, and governed. Rather than continuing with manually built pipelines, the group adopted a metadata-driven approach built on a modern Data Vault architecture. 

VaultSpeed provides a no-code, metadata-centric platform that automates data modeling, integration, and deployment. This allowed Colruyt to align domain-driven design with its broader data mesh vision, while maintaining consistency and governance across brands and business units. 

By automatically ingesting and harmonizing metadata from multiple source systems, the platform established a scalable foundation for forecasting, supply chain optimization, and merchandising analytics. Governance, lineage, and auditability were embedded by design, creating an AI-ready data layer that could be trusted across the organization. 

Without metadata-driven automation, managing billions of records, evolving schemas, and cross-domain relationships at this scale would not have been sustainable. 

Delivering Retail Use Cases at Scale 

With this foundation in place, Colruyt Group moved rapidly from data preparation to real business impact. Delivery timelines for new data products dropped from roughly 3 months to less than 4 weeks. Manual modeling and testing efforts were significantly reduced, generating up to €1.3 million in annual savings. 

Retail forecasting was fully automated, improving on-shelf availability and reducing stock inefficiencies. Store teams now rely on daily, data-driven recommendations instead of manual planning, allowing them to respond faster to demand changes and reduce out-of-stock situations. 

Store-level demand forecasting achieved 84% accuracy even before opening new locations, giving decision-makers greater confidence in where and how to expand the retail footprint. 

The platform now serves as a single source of truth for more than 150 internal users across 5 service domains, aligning teams around consistent, trusted data. 

Concrete Use Cases Driving the Business 

Colruyt Group applies this data foundation across a range of high-impact retail scenarios. In forecasting and supply chain operations, AI models now determine daily product allocation by store, replacing manual stock planning. For store planning, advanced geospatial analytics predict three-year turnover with 84% accuracy, guiding decisions on where to open new locations. 

Sustainability initiatives benefit from data-driven eco-scores that provide transparency into product-level environmental impact, strengthening customer trust and informed choice. At the same time, innovations such as smart shopping carts and the Xtra loyalty app use real-time data streams to deliver more personalized and engaging shopping experiences. 

Governance, Ethics, and Responsible AI 

As advanced analytics and AI became more embedded in daily operations, strong governance became a prerequisite for moving faster, not a constraint on innovation. As data and AI became more central to operations, Colruyt Group embedded ethics and governance into its strategy. An internal AI policy and advisory board guide responsible data use, while a multidisciplinary governance team evaluates data initiatives for legal, societal, and ethical alignment. 

This approach reflects the group’s core values of simplicity, empowerment, and connection, ensuring that innovation supports both business outcomes and societal responsibility. 

The Impact: Faster Delivery with Trust Built In 

The result is not just a modern data platform, but a fundamentally different way of operating with data. Delivery cycles are shorter, forecasting is automated, and new use cases can be introduced without destabilizing existing systems. Scale no longer introduces fragility. It reinforces consistency and trust. 

The result is a retail data platform that supports predictive decision-making, accelerates innovation, and aligns teams across the organization. Scale no longer introduces fragility. Instead, it becomes a competitive advantage. 

The Takeaway: A Blueprint for Data-Driven Retail 

Colruyt Group demonstrates how large retailers can turn extreme data volumes into actionable insight by rethinking their data operating model. By combining a modern Data Vault architecture with metadata-driven automation, the group created a foundation that supports forecasting, sustainability, and personalized customer experiences at scale. 

As Colruyt Group summarized the transformation: 

“Thanks to VaultSpeed, we industrialized our data layer without compromising agility or compliance. Our teams build and deploy data products faster, smarter, and at scale.” 

For retailers facing growing data volumes, complex domains, and rising expectations for AI-driven insight, this case shows how automation and governance can move forward together. At Colruyt’s scale, automating the data layer was not about efficiency alone. It was the only way to combine retail speed, predictive insight, and responsible governance in a single operating model.