From Data Lake to an AI-Ready Enterprise Data Platform
Case Study
How a European Fashion Marketplace Standardized Business Metrics and Prepared Its Data for AI
A large European fashion marketplace had hundreds of teams producing and consuming analytical data. While the organization had already invested heavily in cloud data infrastructure, business logic remained fragmented across dashboards, SQL queries and individual data products.
A major challenge emerged around metric consistency: different teams could calculate the same business KPI differently.
Dateonic designed a governed Databricks architecture inspired by the modern data foundation pattern used by large-scale digital retailers. The solution separated data creation from consumption, introduced centralized governance and established a semantic layer for trusted business metrics.
Turnover
8+ Months
Industry
E-commerce platform
Technology:
Azure Databricks, Delta Lake, Unity Catalog
Federated Data with Central Governance
Domain teams retained autonomy over the creation of their data products, while shared data was exposed through a centrally governed layer. Unity Catalog provided centralized security, discoverability and lineage.
This follows the architectural principle described by Zalando: private environments allow teams to move quickly, while shared governed datasets provide a trusted company-wide consumption layer.
Defining Metrics Once
Business metrics such as revenue, orders, margin and cancellations were moved out of individual dashboards and into a governed semantic layer. Metric definitions included ownership, documentation and validation, allowing the same logic to be reused across BI, SQL and AI workloads.
This directly reflects Zalando’s current “metrics as code” approach using Databricks Metric Views.
Making Enterprise Data AI-Ready
Once the semantic layer was established, conversational analytics could be introduced on top of governed business definitions. Instead of asking an AI system to infer business logic from raw tables, users could query trusted metrics and dimensions.
Business Impact
- Federated data ownership
- Centralized governance
- Consistent KPI definitions
- Reduced dashboard sprawl
- Reusable semantic layer
- AI-ready analytical foundation
- Natural-language access to trusted business data