Problem Statement
Before our engagement, the client faced several technical and operational challenges in their existing reporting and data management landscape:
- Fragmented Data Sources: Insurance data was sourced from multiple operational systems with inconsistent schemas and transformation logic.
- Limited Scalability: The on‑premises reporting environment struggled with increased data volumes and complex analytical workloads.
- Rigid ETL Architecture: Existing ETL processes required manual package changes whenever source mappings were modified, increasing maintenance effort and risk of errors.
- Performance Issues: Reporting and analytics queries were slow, impacting business users and leadership dashboards.
- Lack of Cloud Readiness: The legacy data warehouse was not designed for future cloud migration, limiting modernization opportunities.
Process
To address these challenges, we implemented a phased Data Warehouse and BI transformation strategy focused on scalability, flexibility, and analytics enablement.
The engagement began with designing and implementing an on‑premises Enterprise Data Warehouse (EDW) on Microsoft SQL Server. We developed a robust ETL framework using SSIS, designed to support configuration‑driven and metadata‑based mappings. This design ensured that any source system changes, such as adding or modifying mappings, could be handled through configuration updates without altering SSIS packages.
To enable analytical reporting, we created SSAS Cubes and Data Marts, optimized for insurance KPIs such as policy performance, claims ratios, premium analysis, and customer metrics. These semantic models allowed business users to access consistent and governed data.
As part of the modernization roadmap, we planned and executed the migration of the on‑premises Data Warehouse to the cloud. The entire EDW was migrated to Snowflake on Microsoft Azure, leveraging cloud scalability, performance, and enhanced security. Azure Data Factory (ADF) pipelines were introduced alongside SSIS to orchestrate cloud‑based data integration workloads.
Finally, we implemented Power BI as the primary BI and visualization platform. Interactive dashboards and reports were developed to provide near real‑time insights for business stakeholders, leadership, and operations teams.
Roles and Responsibilities
- Designed and managed a scalable, secure, and highly available database architecture for insurance analytics.
- Optimized database and ETL performance to support large‑scale analytical workloads.
- Established best practices and guided the team to develop high‑quality, maintainable database and ETL code.
- Developed and managed SSIS and Azure Data Factory pipelines for on‑premises and cloud environments.
- Led Azure cloud architecture management for data integration and warehousing.
- Implemented and supported Power BI and other BI technologies for enterprise reporting.
- Executed Snowflake implementation and migration from on‑premises Data Warehouse to cloud‑based Data Warehouse.
Outcome
The solution delivered a modern, scalable, and analytics‑driven data platform for the insurance client, significantly improving reporting efficiency and decision‑making capabilities.
Key Outcomes
- Dynamic & Low‑Maintenance ETL Architecture – Configuration‑driven SSIS design eliminated the need for package changes when source mappings evolved.
- Improved Performance & Scalability – Optimized data models and Snowflake migration enabled faster queries and supported growing data volumes.
- Cloud‑Ready Data Platform – Successful transition from on‑premises EDW to Snowflake on Azure, ensuring long‑term scalability and cost efficiency.
- Enhanced Business Visibility – Power BI dashboards provided interactive, self‑service analytics for business and leadership teams.
- Operational Efficiency – Reduced manual intervention, faster development cycles, and improved system reliability.Future‑Ready BI Ecosystem – The platform now supports advanced analytics, regulatory reporting, and future data science initiatives.
- Future‑Ready BI Ecosystem – The platform now supports advanced analytics, regulatory reporting, and future data science initiatives.
Technology Stack
- Data Warehouse & Database: MS SQL Server, Snowflake
- ETL & Data Integration: SSIS, Azure Data Factory
- Analytics & BI: Power BI, SSAS Cubes
- Cloud Platform: Microsoft Azure
- Architecture & Governance: Metadata‑driven ETL, Config‑based mappings
