Establishing a scalable enterprise-wide data platform for data-driven logistics at a global scale.
Case Study
Enterprise Data Platform
Rhenus Group
Establishing a scalable enterprise-wide data platform for data-driven logistics at a global scale.
Rhenus Group operates across multiple business units, regions, and logistics domains including Air & Ocean, Warehousing, Road Freight, and Contract Logistics. Data was distributed across numerous operational systems such as TMS, WMS, ERP, CRM, and additional regional applications.
This fragmented landscape led to limited transparency, inconsistent KPIs, and high effort for reporting and analytics. In particular, sales data and pipeline reporting from CRM systems played a central role but were not consistently integrated with operational data. As a result, end-to-end visibility across sales, operations, and financial performance was limited.
Data had to be manually consolidated, and insights were often delayed. Different business units used their own data structures, making cross-domain reporting difficult. At the same time, the growing demand for AI use cases, automation, and advanced analytics required a scalable and unified data foundation.
Existing data warehouse structures were not designed to handle increasing data volumes, real-time processing, and AI workloads. The objective was to establish a scalable enterprise-wide data platform that integrates operational and sales systems, standardizes data, and enables analytics and AI across the organization.
The goal was to design and implement a centralized Enterprise Data Platform to unify data across the Rhenus Group and provide a scalable foundation for analytics, reporting, and AI.
The platform needed to integrate operational systems such as TMS, WMS, ERP, and CRM to enable end-to-end visibility from sales pipeline to operational execution and financial performance. A consistent KPI model across business units was required to support global reporting and management steering.
Another key objective was the introduction of a clear data architecture and data domain model. The platform needed to follow a structured data platform blueprint with defined data domains, ownership, and standardized data services. These data services should be consumed by business units for reporting, analytics, and AI use cases.
Additionally, the platform needed to support both batch and near real-time processing, enable self-service analytics, and provide an AI-ready data foundation. Scalability, governance, and long-term maintainability were key requirements.
NEOZO designed and implemented a centralized Enterprise Data Platform based on a modern Data Lakehouse architecture. The platform integrates data from operational and commercial systems including TMS, WMS, ERP, CRM, and additional business applications.
Data is ingested, standardized, and structured into domain-based data models. A clear data platform blueprint defines data domains, ownership, and transformation logic. Curated data layers provide standardized KPIs and harmonized business metrics across the organization.
The platform exposes data services that are consumed by business units for reporting, dashboards, and analytics. These data services provide consistent and governed access to business-critical data such as sales performance, operational KPIs, and financial metrics.
The architecture supports both batch and near real-time ingestion and enables scalable analytics and AI workloads. The Enterprise Data Platform provides a unified foundation for reporting, analytics, and advanced data-driven use cases.
Integration of operational and commercial data across TMS, WMS, ERP, and CRM systems.
Modern architecture combining scalability, flexibility, and governance.
Clear data domains with defined ownership and standardized transformations.
Curated data services consumed by business units for reporting and analytics.
End-to-end visibility from CRM pipeline to operational execution.
Scalable foundation for predictive analytics, AI, and automation use cases.
Central governance model with consistent KPIs and improved data quality.
The Enterprise Data Platform is built on a modern, scalable, and open architecture using primarily open-source technologies. The platform follows a Data Lakehouse approach and supports batch and streaming ingestion.
Technology selection is based on scalability, performance, and total cost of ownership. Open standards are prioritized to ensure flexibility and avoid vendor lock-in. The architecture supports domain-driven data modeling, data services, and AI workloads.
The Enterprise Data Platform provides a unified data foundation across the Rhenus Group and enables data-driven decision-making across business units.
Operational and sales data are now integrated into a centralized platform. CRM pipeline data, operational KPIs, and financial metrics are harmonized and available through standardized data services. Business units can consume curated datasets for reporting and analytics.
The platform enables consistent KPI definitions, improved transparency, and faster reporting cycles. Data services allow business units to build dashboards and analytics without duplicating transformation logic.
The solution provides a scalable foundation for AI and advanced analytics while reducing manual reporting effort and improving data quality.
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