Consolidating Fragmented Cloud and On-Prem Data Platforms
Rationalizing Hybrid Data Environments to Reduce Complexity and Cost
- Service: Data Modernization & Cloud Platforms
- Industry: Mid-Market Enterprise (Multi-Business Unit Organization)
- Location: Chicago, IL, USA
Executive Summary
Organizations that grow through acquisitions often inherit fragmented data platforms across cloud and on-premise environments, increasing complexity and cost. SLOANCODE partnered with a mid-market enterprise to rationalize its hybrid data landscape into a simplified, governed architecture. This transformation reduced operational overhead, improved data consistency, and delivered measurable cost savings.
Client Overview
The client, a mid-market enterprise operating across multiple business units, managed a complex mix of cloud platforms and on-premise databases. Redundant systems, duplicate pipelines, and inconsistent reporting created inefficiencies and rising costs. As a result, the organization lacked visibility into its data platform landscape and struggled to scale efficiently.
The Challenges
- Limited visibility into total data platform cost and usage
- Inconsistent data definitions across business units
- Operational overhead from maintaining redundant platforms and pipelines
Implementation Process
Data Environment Assessment & Cost Analysis
Mapped existing platforms, usage patterns, and cost drivers to identify consolidation opportunities and inefficiencies.
Target Architecture & Rationalization Design
Designed a simplified, unified data architecture consolidating platforms, pipelines, and data models across environments.
Integration & Platform Consolidation
Standardized data pipelines and integrated systems to ensure consistent data flow and reporting across business units.
Migration, Decommissioning & Optimization
Executed phased migration and decommissioning of redundant systems while optimizing performance, cost, and governance controls.
The Solution Provided
We delivered a data platform rationalization solution focused on simplification, governance, and cost efficiency:
- Platform Rationalization: Reduced redundant cloud and on-prem systems to streamline architecture
- Unified Data Architecture: Standardized pipelines, data models, and integration patterns
- Cost Optimization & Governance: Established visibility, controls, and policies to manage usage and reduce waste
Why This Approach Worked
We applied a rationalization-first modernization approach to reduce complexity before scaling. By consolidating platforms, standardizing architectures, and implementing governance controls, we improved maintainability, reduced cost, and enabled a more efficient and scalable data environment.
Technology Stack
- Cloud Platforms (Azure / AWS)
- Hybrid Cloud Data Architecture
- Cloud Data Warehouse / Lakehouse Architectures
- Data Integration Pipelines (ETL / ELT)
- Real-Time & Batch Data Processing Frameworks
- SQL & Python
- Data Modeling & Transformation Layers
- Metadata, Lineage & Data Catalog Tools
- Data Governance & Quality Frameworks
- Cost Monitoring & Optimization Tools
- Role-Based Access Control (RBAC) & Security Controls
- API Integration Layer (REST / GraphQL)
- Monitoring & Observability Tools
- Audit Logging & Compliance Frameworks
- Analytics & BI Platforms (Tableau, Power BI)
Results Achieved
- 35% reduction in total data platform costs
- Simplified architecture with fewer failure points
- Improved reporting consistency across business units
- Enhanced visibility and control over data platform usage
Team Members and Skillsets
- 1 Data Platform Lead (Architecture and rationalization)
- 2 Data Engineers (Integration and migration)
- 1 Cloud Cost Analyst (Optimization and governance)
- 1 BI Specialist (Reporting validation)
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