case study

On-prem Oracle Exadata data warehouse migration to BigQuery


Business Impacts


Infrastructure & Maintenance cost savings


Growth in the analytics business

150 TB

Data migrated


Data Mart tables added

Customer Key Facts

  • Country : America
  • Industry : Insurance
  • About : The client is a leading U.S. - based global commercial property and casualty insurance


  • The client wanted a model orchestrator that will integrate with the Claim Center/Rules Engine and Support the deployment of clients’ flag library & deploying predictive models in Vertex AI.
  • Client also wanted a ML model that will give scoring to the claims based on severity (High, medium, low), once the FNOL notification has came
  • Preparation of input data for ML model – combining data points from uploaded structured and unstructured documents with legacy data from client database
  • Integration with multiple modules

Technologies Used

Cloud Storage

Cloud Storage



Data Fusion

Data Fusion












  • Quantiphi migrated 150 terabytes of data from Oracle Exadata to Google’s BigQuery.
  • Built a claim center data warehouse on GCP
  • Developed an enterprise-grade DLP solution


Significant Cost Savings

60% Reduction in Infrastructure & Maintenance Costs: The migration from Oracle Exadata to Google’s BigQuery resulted in substantial cost savings in infrastructure and maintenance, reducing overall operational expenses.

Improved Claim Processing

  • Integrated Claim Center Data Warehouse: Built a comprehensive claim center data warehouse on GCP, enabling better integration with the Claim Center/Rules Engine.
  • Predictive Model Deployment: Developed and deployed predictive models in Vertex AI for scoring claims based on severity (high, medium, low) upon First Notice of Loss (FNOL).

Expanded Data Infrastructure

  • 21,000 Data Mart Tables Added: The addition of 21,000 data mart tables expanded the client's data infrastructure, providing a robust foundation for advanced analytics and reporting.

Data Integration and Preparation

  • Combining Structured and Unstructured Data: Prepared input data for machine learning models by integrating data points from uploaded structured and unstructured documents with legacy data from the client's database.
  • Seamless Module Integration: Achieved seamless integration with multiple modules, enhancing the overall functionality and efficiency of the system.

Enhanced Analytical Capabilities

  • 3x Growth in Analytics Business: The migration facilitated a threefold increase in the analytics business, allowing the client to leverage enhanced data processing and analytical capabilities.
  • 150 TB Data Migrated: Successfully migrated 150 terabytes of data, ensuring seamless continuity and accessibility of historical data.

Enterprise-Grade Data Security

Enterprise-Grade DLP Solution: Developed a robust Data Loss Prevention (DLP) solution, ensuring the security and compliance of sensitive data throughout the migration and beyond.

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