A leading U.S.-based healthcare services provider operating across 400+ surgical facilities was looking to modernize their legacy data warehouse platform to support scalable, secure and data-driven operations. Partnering with HCLTech and Google Cloud, the organization embarked on a comprehensive cloud transformation to improve data accessibility, accelerate analytics insights and reduce operational costs. Through automation-led delivery and deep expertise in Google Cloud, HCLTech helped the client unlock agility and actionable insights across its enterprise data landscape.
The Challenge
- Limited analytics capabilities and time-consuming data processing workflows
- Inflexible legacy MSSQL-based systems driving up total cost of ownership (TCO) and maintenance overhead
- Need for a scalable, cloud native architecture to accommodate multiple EMR (Electronic Medical Record) data sources
- Complex data integration requirements spanning across multiple SQL Servers, AWS, Oracle and other disparate systems
The Objective
- Enable seamless modernization of the enterprise data warehouse to Google Cloud
- Improve data analytics speed, scalability and cross-system integration
- Reduce TCO through automation and optimize infrastructure utilization
- Enhance compliance, governance and access to patient data for better clinical insights
The Solution
- Automated infrastructure provisioning and landing zone setup using Terraform
- Designed and deployed a scalable data ingestion framework leveraging Google Cloud Dataflow
- Migrated the legacy data warehouse to Google BigQuery for advanced analytics and query performance
- Integrated 150+ data sources across SQL Server, AWS, Oracle and other platforms into a unified, cloud native ecosystem
The Impact
- 50% increase in ETL processing speed and 70% faster query response with BigQuery
- 60% reduction in data ingestion time, freeing up 30–40% of data engineering capacity
- 20% lower TCO through automation and cloud native modernization
- Enhanced compliance and improved access to patient data for more informed, real-time clinical decision-making
