Founded in Asia in 2013, this leading life insurance company has emerged as a pioneer in the industry, committed to enhancing customer experiences through innovative, technology-driven solutions.
Key Challenges
The company faced significant data fragmentation across more than 50 source systems. This lack of integration hampered decision-making, reduced operational efficiency, and limited opportunities for data monetization. To drive innovation and improve agility, the company needed a unified data solution.
Key Requirements
- Data Consolidation: Integrate and streamline data across departments to eliminate silos.
- Timely Decision-Making: Deliver relevant, real-time insights to key stakeholders.
- Operational Efficiency: Improve reconciliation, data quality, and transparency.
- Hyper-Personalization: Leverage behavioral data from all customer touchpoints to deliver highly personalized experiences.
Our Solution
Blazeclan implemented a powerful, scalable solution to address the company’s data challenges and strategic goals:
- AI & Analytics Use Cases: Deployed 29 advanced AI and analytics use cases to unlock deeper value from enterprise data.
- Unified Data Hub (UDH): Created a centralized hub delivering real-time insights across all touchpoints. The UDH improved decision-making and monetized previously untapped data.
- Optimized Data Investment: Maximized ROI from the data lake and warehouse by enabling smarter insights and driving operational efficiency.
Our Approach
Strategic Integration
- Databricks on AWS: Built a unified data platform leveraging Databricks Lakehouse on AWS, merging the best of data warehouses and lakes for simplified management and powerful analytics.
- Microservices API: Developed and deployed Microservices APIs to support near real-time data consumption with minimal latency.
- AWS Native Services: Implemented comprehensive monitoring and logging with AWS CloudWatch, SNS, and CloudTrail for enhanced security and performance.
Databricks Lakehouse Architecture Advantages
- Unified Analytics: Supports in-depth analysis of both structured and unstructured data.
- Versatile Workloads: Enables real-time streaming and machine learning for advanced insights.
- Scalability: Handles large volumes of data with horizontal scaling.
- Data Consistency: Maintains data reliability using ACID transactions.
- Simplified Architecture: Eliminates the need for separate data lakes and warehouses.
- Cost Efficiency: Delivers robust analytics with lower storage costs.
Outcomes
Lead Cleansing & Enrichment
- Built a dynamic dashboard processing over 2.7 million leads.
- Generated 80.5 million THB in annual premium equivalent.
- Reduced turnaround time by 96%—from 24 hours to under 1 hour.
Event-Driven Data Streaming for AI-Based Underwriting
- Enabled real-time data triggers for faster processing.
- Improved accuracy by transforming source data from JSON to AVRO format.
- Replaced legacy systems with real-time streaming to boost data precision.
Customer 360 Dashboard Migration
- Migrated from Tableau to Power BI for enhanced performance.
- Enabled features like quick search, multi-language support, customer profiles, policy overviews, and sales insights.
Tech Stack
- Cloud Platform: AWS Public Cloud Hosting
- Data Platform: Databricks Lakehouse
- Data Streaming: Confluent Kafka
- Data Engineering: Spark Streaming & Batch Workloads
- API Management: Amazon EKS, API Gateway
- Advanced Analytics & Reporting: Power BI
- MLOps: Databricks MLOps Workflows
- Database Management: AWS RDS
- Monitoring & Logging: AWS CloudWatch, SNS, DevOps Tools