Retail Chain Modernizes Data Platform with SAP Datasphere

How a major retail organization migrated from legacy BW to SAP Datasphere, reducing infrastructure costs by 40% while improving data accessibility

The Challenge

A national retail chain with 250+ stores across North America was running a legacy SAP BW 7.4 system that had become a bottleneck for their analytics initiatives. The aging infrastructure was expensive to maintain, difficult to extend with new data sources, and lacked the agility needed for modern analytics use cases.

Critical Issues

  • High TCO: Annual infrastructure and maintenance costs exceeded $800K
  • Performance Degradation: Query response times averaged 15-30 seconds for standard reports
  • Limited Accessibility: Business users couldn’t access data without IT intervention
  • Integration Complexity: Adding new data sources (e.g., e-commerce, loyalty apps) required months of development
  • Lack of Self-Service: Data analysts spent 60% of their time requesting IT to build reports

Our Solution

We designed and executed a comprehensive migration from SAP BW 7.4 to SAP Datasphere, leveraging cloud-native capabilities while preserving critical business logic and historical data.

Migration Strategy

Phase 1: Assessment & Planning (4 weeks)

  • Inventoried 450+ BW objects (InfoCubes, DSOs, transformations)
  • Identified 85 critical reports used daily by business stakeholders
  • Analyzed data lineage for 12 source systems
  • Created migration wave plan prioritizing high-impact, low-complexity objects

Phase 2: Datasphere Foundation (6 weeks)

  • Provisioned SAP Datasphere tenant with appropriate sizing
  • Established connectivity to SAP S/4HANA, e-commerce platform, and loyalty system
  • Designed space architecture aligned with business domains (Sales, Inventory, Customer)
  • Implemented role-based access controls and data privacy policies

Phase 3: Data Model Migration (12 weeks)

  • Migrated 120 high-priority BW objects to Datasphere views
  • Converted ABAP transformations to SQL-based data flows
  • Rebuilt composite providers as analytical datasets
  • Validated data accuracy through reconciliation testing (99.7% match rate)

Phase 4: Report & Analytics Migration (8 weeks)

  • Migrated 85 critical reports from BEx to SAC stories
  • Enabled self-service access through Datasphere’s business layer
  • Built new dashboards leveraging Datasphere’s federation capabilities
  • Integrated external data sources (Google Analytics, social media sentiment)

Phase 5: Cutover & Optimization (4 weeks)

  • Executed parallel run with legacy BW for validation
  • Trained 50+ business users on self-service capabilities
  • Decommissioned legacy BW infrastructure
  • Fine-tuned Datasphere models based on usage patterns

Technical Architecture

Data Sources          Datasphere Layers        Consumption
• SAP S/4HANA    →   • Data Layer         →   • SAC Dashboards
• E-commerce DB  →   • Business Layer     →   • Self-Service Reports
• Loyalty App    →   • Analytic Models    →   • Microsoft Excel
• Google Analytics →  • Federation Layer   →   • Third-party BI Tools

The Results

Cost & Performance Improvements

  • 40% Reduction in total cost of ownership (infrastructure + maintenance)
  • 5x Faster Queries: Average response time improved from 20s to 4s
  • 90% Reduction in data provisioning time (days to hours)
  • Zero Infrastructure Management: Eliminated on-premise server maintenance

Business Impact

  • Self-Service Adoption: 50+ business users now create their own reports without IT support
  • Real-Time Inventory Visibility: Store managers access live stock levels across all locations
  • Unified Customer View: Combined POS, e-commerce, and loyalty data in single customer profiles
  • Faster Decision-Making: Merchandising team analyzes trends 3x faster than before

Data Democratization

  • 300+ New Data Consumers: Non-technical users gained direct access to curated data
  • 15+ New Data Sources Integrated: Including external market data and social media
  • 70% Reduction in IT support tickets for data access requests

Technologies Used

  • SAP Datasphere (Cloud Edition)
  • SAP Analytics Cloud
  • SAP S/4HANA (Live Connection)
  • REST API Connectors
  • SQL-based Data Flows
  • Datasphere Business Layer

Client Testimonial

“The migration to SAP Datasphere was a game-changer. Not only did we cut costs significantly, but we also empowered our business teams to become truly data-driven. Our merchandising team now spots trends within hours instead of weeks, giving us a competitive edge.”

— Chief Data Officer, National Retail Chain

Key Takeaways

  1. Wave-Based Migration: Prioritizing high-value, low-complexity objects first built momentum and user confidence
  2. Data Quality First: Rigorous reconciliation testing prevented data trust issues post-migration
  3. Business Layer is Critical: Semantic layer enabled true self-service without exposing technical complexity
  4. Change Management: User training and adoption support were as important as technical execution
  5. Cloud Benefits: Eliminated infrastructure headaches while improving performance and scalability

Lessons Learned

  • Don’t Lift-and-Shift: Redesign data models to leverage Datasphere’s modern capabilities rather than replicating BW architecture.
  • Federation is Powerful: Ability to query data in place (without moving it) accelerated time-to-value.
  • Involve Business Early: Business stakeholder participation in design prevented rework and improved adoption.

Planning a data platform modernization? Contact us to learn how we can help you migrate to SAP Datasphere with minimal risk and maximum value.