Building a Real-Time Retail Control Tower
Case Study
How an Omnichannel Retailer Connected Commerce, Inventory and Supply Chain Data
A large omnichannel retailer operated a complex network of stores, distribution centers and digital sales channels. Data about orders, inventory, fulfillment, transportation and returns was spread across operational systems, creating delays in identifying issues and reacting to changes in demand.
Dateonic implemented a Databricks-based real-time data platform that unified transactional and operational data and created the foundation for supply chain analytics, inventory optimization and predictive AI.
Turnover
8+ Months
Industry
E-commerce platform
Technology:
Azure Databricks, Delta Lake, Unity Catalog
Creating Real-Time Operational Visibility
Order, inventory, warehouse and logistics events were continuously ingested into the Lakehouse. This replaced multiple disconnected batch processes with a common data foundation capable of supporting both streaming and analytical workloads.
From Reporting to Prediction
The platform enabled demand forecasting, inventory allocation, stockout prediction and operational anomaly detection. Models could use current sales and inventory signals rather than relying exclusively on historical snapshots.
Building a Retail Control Tower
Business users received a unified view of the operational state of the organization, from incoming orders and warehouse capacity to inventory availability and delivery performance.
The architecture follows the retail control-tower pattern described in the Databricks reference material, where real-time supply chain data enables companies to identify and respond to operational problems as they happen.
Business Impact
- Near real-time operational visibility
- Unified commerce and supply chain data
- Faster identification of operational issues
- Predictive inventory management
- Supply chain control-tower foundation
- Common platform for BI, ML and AI