Case Study

Transforming Data Intelligence

Situation Of the Client:

This case study is a property of Azure Synapse Analytics.

Swiss Re, a global leader in reinsurance, faced significant challenges with its data intelligence and reporting infrastructure. The company’s existing system, centered around an in-house solution called Cockpit, was becoming obsolete, failing to meet the growing demands for quick, flexible, and comprehensive data insights. This case study explores how Swiss Re transformed its data analytics and reporting capabilities by integrating Azure Synapse Analytics and Power BI into its operations.

medical professional comparing reports

Solution provided

Recognizing the need for a more robust and scalable data intelligence solution, Swiss Re turned to Microsoft’s Azure Synapse Analytics and Power BI. The transition involved several key steps:

  1. Data Migration and Integration: Data was migrated from existing databases to Azure. Azure Data Factory and Azure Data Lake Storage were used to consolidate various data types, and Azure Databricks facilitated the processing of structured and unstructured data sets.
  2. Infrastructure Enhancement: Azure Synapse’s massive parallel processing architecture significantly improved data processing capabilities, allowing for quicker and more efficient data analysis.
  3. Reporting and Insights: Power BI services were implemented to ensure a consistent and reliable data source for all Property & Casualty reinsurance data. This integration enabled more dynamic and insightful reporting, aligning with the specific needs of different business units.
  4. User Interface Improvement: The Cockpit interface was revamped to offer a more user-friendly experience, incorporating advanced features like machine learning and data engineering.

business outcomes

The Azure Synapse Analytics and Power BI integration transformed Swiss Re’s operations:

  1. Data Processing Speed: Load chain shortened by 5 hours, data loading 40% faster.
  2. Cost Efficiency: Significant operational cost reduction, 30-40% savings on reserved Azure services.
  3. Reporting System Improvement: Dashboards creation time reduced from months to days, enhancing decision-making speed.
  4. Scalability: Rapid adoption across divisions, confirming the solution’s scalability and effectiveness.
Data Dashboard and charts
*Based On Client Scenario
Reduced Data Loading Time
600 Mn
Rows in huge database
Reduced Operational Costs

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