Azure data modernization to eliminate silos and accelerate self-service insights
A US-based agriculture enterprise needed to modernize a legacy reporting environment dependent on an end-of-life BI tool. Data was fragmented across systems, reporting cycles were slow, and self-service access was limited. CES implemented a scalable Azure data platform that unified reporting data and delivered enterprise dashboards for 2000+ users—improving visibility, reducing manual effort, and enabling faster decision-making.
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The Challenge
Multiple Data Silos
Legacy Reporting Ecosystem at End-of-Life
Limited Self-Service BI for 2000+ Users
the client
Agriculture (Agri-retail)
Technology Stack
- Azure Data Factory
- Azure Data Lake
- Delta Lake
- Synapse Analytics
- Power BI
Solution Area
- Data & Analytics | Cloud Data Platform Modernization
the impact
Single Source of Truth for Reporting
Self-Service BI Enablement
Faster Decision-Making
Scalable Architecture for Future Growth
The shift was data-platform-led.
The result: unified, self-service reporting.
The Need
The organization required a modern, integrated analytics foundation to replace an end-of-life reporting platform and standardize reporting across departments. The goal was to consolidate fragmented reporting data, improve visibility, and enable self-service BI—without compromising security, governance, or performance for a large user base.
Challenges
- Multiple Data Silos: Data was distributed across systems, creating inconsistencies, duplication, and delays in reporting cycles.
- Legacy Reporting Dependency (End-of-Life Risk): Continued reliance on the legacy reporting tool introduced support and compatibility risks, limiting extensibility and modernization.
- Limited Visibility and Self-Service Analytics: Business teams lacked a unified view across functions, impacting operational efficiency, planning, and ROI tracking—especially at scale.
CES modernized the reporting ecosystem using an Azure-native data platform designed for scale, governance, and self-service.
Modern Scalable Data Architecture
- Built ingestion and ETL pipelines using Azure Data Factory
- Centralized storage using Azure Data Lake with Delta Lake to consolidate reporting datasets with reliability and version control
- Implemented an analytics-ready warehouse layer using Synapse DW for analytical workloads
Unified Platform for Cross-Department Reporting
- Standardized reporting datasets to reduce fragmentation and improve consistency
- Established shared definitions for key reporting metrics and KPIs
Enterprise BI with Power BI
- Delivered role-based dashboards with drill-down reporting
- Enabled near real-time visibility through centralized datasets and curated semantic structures
Phased Implementation Methodology
- Initiation: identified limitations in the existing reporting ecosystem; aligned on KPI priorities
- Inception: assessed source systems, report dependencies, and user roles; defined target architecture
- Elaboration: planned migration from legacy reporting to Power BI; finalized pipeline/storage approach
- Construction: built multi-source ingestion into the Azure platform; created curated datasets; validated performance for 2000+ concurrent users
- Production: rolled out across departments; enabled self-service access; implemented governance and monitoring for sustainability
- Single Source of Truth: consolidated reporting data into a unified, governed foundation
- Self-Service BI Enablement: improved business access to dashboards and analytics without heavy reporting dependency
- Improved Productivity and ROI Visibility: teams gained actionable insights with reduced manual reporting effort
- Scalable Architecture: platform built to support growth in data volume, adoption, and advanced analytics readiness