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Case Study: Healthcare Data Warehouse Migration to Azure Cloud
Problem Statement: Legacy On-Premise Data Architecture Limiting Scalability
A leading healthcare organization serving children with Autism struggled with its legacy on-premise data warehousing infrastructure. The existing system was inefficient, slow, and lacked modern governance, resulting in:
8-Hour ETL Cycles
Manual SSIS-based ETL jobs ran overnight, delaying data availability for stakeholders.
Manual SSIS-based ETL jobs ran overnight, delaying data availability for stakeholders.
Fragmented Data Sources:
Business-critical data was dispersed across disconnected systems, limiting integrated analytics.
Business-critical data was dispersed across disconnected systems, limiting integrated analytics.
Manual File Dependencies
Heavy reliance on manual file uploads introduced operational risks and increased labor costs.
Heavy reliance on manual file uploads introduced operational risks and increased labor costs.
Lack of Data Governance
No formal relationship existed between operational data and business KPIs, complicating reporting.
No formal relationship existed between operational data and business KPIs, complicating reporting.
Duplicated Reports & Inconsistent Insights
Business units maintained separate report sets, leading to redundant efforts and unreliable analysis.
Business units maintained separate report sets, leading to redundant efforts and unreliable analysis.
Low User Adoption
A slow and clunky system resulted in minimal business user engagement with analytics platforms.
A slow and clunky system resulted in minimal business user engagement with analytics platforms.
Impact: Downtime, Delayed Decisions & Analytical Blind Spots
These operational shortcomings led to significant consequences:
High Downtime
Long ETL cycles meant key dashboards were unavailable for most of the business day.
Long ETL cycles meant key dashboards were unavailable for most of the business day.
Delayed Analytics
Inability to consolidate data from various sources hindered timely insights.
Inability to consolidate data from various sources hindered timely insights.
Increased Manual Overhead
Manual file handling drained IT and business team bandwidth.
Manual file handling drained IT and business team bandwidth.
Inconsistent Data & Reporting
Multiple versions of the truth led to confusion and poor decision-making.
Multiple versions of the truth led to confusion and poor decision-making.
Solution: Cloud-Native Azure Data Warehouse Modernization
To overcome these bottlenecks, JBS engineered a modern, cloud-first solution leveraging the Azure Data Platform:
Azure Data Factory (ADF)-Powered ETL
ETL pipelines were rearchitected using ADF, cutting data load times from 8 hours to under 40 minutes.
ETL pipelines were rearchitected using ADF, cutting data load times from 8 hours to under 40 minutes.
Scalable Azure Data Warehouse (DWH)
Migrated the legacy warehouse to Azure Synapse, ensuring high performance, elastic scaling, and centralized access.
Migrated the legacy warehouse to Azure Synapse, ensuring high performance, elastic scaling, and centralized access.
Control Database for Source Management
Introduced a metadata-driven control DB to manage multi-source ingestion and simplify system maintenance.
Introduced a metadata-driven control DB to manage multi-source ingestion and simplify system maintenance.
Automated File Integration:
Enabled automatic ingestion of previously manual files using Logic Apps and centralized monitoring tools
Enabled automatic ingestion of previously manual files using Logic Apps and centralized monitoring tools
Enterprise Data Model & KPI Framework
Created business-aligned data models to standardize KPI definitions and align analytics with strategic goals.
Created business-aligned data models to standardize KPI definitions and align analytics with strategic goals.
Consolidated Enterprise Dataset
Developed a single, governed dataset to eliminate redundant reporting and improve self-service BI adoption.
Developed a single, governed dataset to eliminate redundant reporting and improve self-service BI adoption.
Business Benefits: Scalable Growth & Operational Excellence
The migration to Azure unlocked measurable improvements across the organization:
80% Reduction in ETL Time:
From 8 hours to just 40 minutes, enabling near real-time reporting.
From 8 hours to just 40 minutes, enabling near real-time reporting.
90% Increase in DWH Uptime:
Dashboards and insights are now accessible around the clock.
Dashboards and insights are now accessible around the clock.
1-Day Source Integration:
New data sources can be onboarded in under 24 hours (vs. 2–3 weeks prior).
New data sources can be onboarded in under 24 hours (vs. 2–3 weeks prior).
Reduced Manual File Overhead:
Automated file handling decreased human error and freed up IT staff.
Automated file handling decreased human error and freed up IT staff.
Reduced Support Load:
A unified dataset significantly lowered redundant report development and support tickets.
A unified dataset significantly lowered redundant report development and support tickets.
Boosted User Engagement:
Improved performance and reliability led to wider adoption of analytics across business functions.
Improved performance and reliability led to wider adoption of analytics across business functions.
Technology Stack
Azure Data Factory
Logic App
Azure Synapse
Azure VM
V-net
Azure Single
Power BI
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