Cloud Migration (3.5 Petabytes) - Teradata to Azure Data Bricks and Snowflake for UnitedHealth Group (UHG)
Cloud Migration (3.5 Petabytes) - Teradata to Azure Data Bricks and Snowflake for UnitedHealth Group (UHG)
UnitedHealth Group (UHG), a leading U.S.-based health insurance and services organisation, initiated a large-scale transformation to modernise its data estate and enable scalable, cloud-based analytics.
Business Challenge
UHG faced significant complexity in migrating and validating enterprise-scale data across platforms while maintaining accuracy and performance. Key challenges included:
- Migration of 3.5 petabytes of data from on-premise Teradata systems to cloud platforms
- Lack of automation for validating migrated data during cloud transformation
- High cost and effort due to manual data testing and validation processes
- Challenges in reconciling data across heterogeneous systems
- Data quality issues, including redundancy and anomalies
- Limited scalability and performance of legacy systems
- Budget constraints limiting adoption of commercial validation tools
HCLTech Solution
HCLTech deployed its AI Foundry platform to deliver a scalable, automated and intelligent data migration and validation framework.
Key elements of the solution included:
- Configuration-driven framework to enable repeatable, large-scale migration
- Use of AI Foundry accelerators to speed up data platform build and migration
- Integration of modular capabilities:
- MigrateData for data migration
- ConvertData for transformation
- CertifyData for validation and reconciliation
- Advanced data validation capabilities:
- Cross-platform comparison between Teradata, Azure Databricks and Snowflake
- High-volume data processing using cloud-native engines
- Automated checks for numeric accuracy, date formats and schema consistency
- Rapid generation of use cases such as table row count comparison across source and target systems
- API-enabled integration with orchestration tools for automation of validation workflows
Business Impact
The engagement delivered measurable improvements in speed, cost efficiency and data quality:
- 50% reduction in time to delivery
- Significant cost optimisation through automated testing processes
- Improved productivity of data migration and testing teams
- Elimination of manual, error-prone validation activities
- Single integrated platform for all data testing and validation use cases
- Stronger collaboration between data migration and testing teams
- 95% of data tables reconciled using AI-led validation
Technologies Used
AI Foundry (MigrateData, ConvertData, CertifyData, PrepData), Azure Databrick and Snowflake
Data Transformation on Azure cloud for UnitedHealth Group (UHG)
Data Transformation on Azure cloud for UnitedHealth Group (UHG)
UHG (UnitedHealth Group), a leading US-based health insurance and services organisation, initiated a large-scale cloud transformation to migrate data workloads from on-premises Teradata systems to Azure-based platforms. However, fragmented data pipelines and duplicated data replication approaches were creating inefficiencies and slowing cloud adoption.
HCLTech partnered with UHG to streamline data transformation and establish a scalable, unified cloud data ecosystem.
Business Challenges
- Multiple analytics and reporting teams created independent pipelines, resulting in duplication and inefficiencies.
- Over 15 applications replicated 1,800+ tables into separate Snowflake and Databricks environments, increasing compute and maintenance costs.
- Repeated data duplication led to unnecessary storage and processing overhead.
- Lack of a centralised data strategy delayed data availability and slowed time-to-insight.
HCLTech Solution
HCLTech implemented a centralised data replication and transformation framework on Azure to modernise UHG’s data ecosystem:
- Established a single, scalable data replication process for enterprise data on cloud.
- Enabled unified data availability across Azure Databricks and Snowflake environments.
- Eliminated redundant pipelines and multiple copies of data across tenants.
- Provided ready-to-consume, integrated datasets to accelerate analytics and reporting.
- Enabled seamless integration of enterprise and non-enterprise data on cloud.
- Accelerated transition from on-prem connectivity to cloud-native operations.
Business Outcomes
- Eliminated pipeline sprawl, significantly reducing operational overhead for data ingestion and management.
- Enabled faster cloud adoption across analytics and reporting teams.
- Supported key initiatives, avoiding approximately $2.2M in claim capital spend.
- Delivered $8.1M in cost avoidance, contributing to long-term efficiency targets.
Impact
By consolidating data pipelines and implementing a unified cloud architecture, HCLTech helped UHG accelerate time-to-value, reduce infrastructure costs and build a robust foundation for enterprise-wide data-driven innovation.
Technologies Used
Microsoft Azure, Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Azure Databricks and Delta Tables, Snowflake, Apache Airflow (on-prem), Terraform, AzCopy.
Enabling data-driven decarbonization with an integrated ESG intelligence platform for Transport for New South Wales (TfNSW)
Enabling data-driven decarbonization with an integrated ESG intelligence platform for Transport for New South Wales (TfNSW)
HCLTech partnered with Transport for New South Wales (TfNSW) to accelerate its sustainability agenda by building a scalable, data-driven ESG platform. The initiative aimed to provide end-to-end visibility into carbon emissions, cost impacts and sustainability metrics across infrastructure projects, enabling informed, future-ready decision-making.
Business Challenge
TfNSW required a unified approach to:
- Gain holistic visibility into carbon footprint across its infrastructure portfolio
- Address fragmented data and lack of a standardised ESG data model
- Align reporting with global ESG and sustainability standards
- Enable scenario analysis to understand cost-to-decarbonise pathways
- centralised insights and reporting
HCLTech Solution
HCLTech designed and delivered a Proof of Value (PoV) to validate a scalable ESG digital solution powered by Microsoft technologies:
- Evaluated Microsoft Sustainability Manager and ESG Data Estate integration
- Established a Common Data Model (CDM) to standardise ESG data
- Implemented robust data validation, quality checks and exception reporting
- Developed intuitive dashboards to visualise carbon, cost and ESG metrics
- Built a scalable data ingestion and processing framework to onboard historical and live data
- Enabled portfolio-level insights and scenario modelling for proactive sustainability planning
Business Impact
- Delivered the PoV 22% under budget, enabling programme extension
- Built a comprehensive ESG data structure with deep insights across multiple domains and sub-sections
- Standardised 1,200+ data attributes to improve data consistency and usability
- Enabled advanced analytics through a scalable, notebook-driven data processing framework
- Empowered stakeholders with actionable insights for carbon, cost and circular economy metrics
Technologies Used
Azure Databricks | Power BI | Azure Data Factory (ADF) | Azure Data Lake Storage (ADLS)
Fonterra's AI-Powered Export Documentation and Data Quality Transformation
Fonterra's AI-Powered Export Documentation and Data Quality Transformation
Fonterra, a New Zealand-based multinational dairy co-operative and the world's largest dairy exporter responsible for 30% of global dairy exports, partnered with HCLTech to revolutionize its export documentation validation process across 140+ countries.
Using Microsoft Azure and generative AI, HCLTech developed an intelligent automation solution that transformed labor-intensive manual validation into streamlined straight-through processing — growing trade volumes while reducing payment risks and operational costs across Fonterra’s global supply chain.
The Challenge
Fonterra faced critical operational challenges managing payment risks across diverse international markets with varying credit environments and complex legal frameworks. The company's reliance on LC for payment guarantees created bottlenecks due to inconsistent, semi-structured SWIFT messages that varied dramatically across countries and banking institutions.
Manual validation of these required highly experienced staff and was becoming unsustainable as trading volumes expanded. The process was particularly challenging given the complexity of multi-product transactions and frequent data changes throughout the sales lifecycle.
The Objective
Recognizing the need for digital transformation to support its growth trajectory, Fonterra sought to automate its LC validation process while maintaining the highest standards of accuracy and compliance. To build on its existing Microsoft technology ecosystem, Fonterra partnered with HCLTech to develop an intelligent, scalable AI solution that could handle the complexity of global trade documentation while providing enterprise-grade security and reliability — so Fonterra could eliminate processing bottlenecks and enable its skilled workforce to focus on higher-value strategic activities.
The Solution
HCLTech delivered a sophisticated GenAI solution built on Microsoft Azure using microservices and event-driven architecture. It leveraged advanced AI capabilities to automatically extract, compare and validate LC data against Fonterra's SAP GTS records, handling the complexity of semi-structured SWIFT messages.
The solution featured intelligent document processing that adapted to:
- Varying international banking formats
- Real-time validation against multiple data sources
- Seamless integration with existing enterprise systems
The platform's responsible AI framework ensured data privacy and security while scaling from single-product transactions to complex multi-product scenarios.
The Impact
The AI-powered solution delivered transformative results that established new operational benchmarks for global trade processing for Fonterra across 140+ countries. The solution achieved:
- Reduces barriers to international commerce: 95% automated straight-through processing for Red Lane transactions
- Significant reduction in staffing requirements: Over 85% accuracy in validation reducing manual intervention
- Enhanced processes: 15% reduction in processing time
- Reduced risk: Improved documentation compliance and reduced payment risks
- Improved employee morale: Redeployment of skilled staff to higher-value strategic roles, improving job satisfaction
Technologies Used
Microsoft Azure, Generative AI, Intelligent Document Processing, Microservices Architecture, Event-Driven Architecture, SAP GTS, SWIFT Message Processing, Responsible AI Framework