![case study](https://cdn.quantiphi.com/2024/02/6f15c31d-business-intelligence-analytics-platform-on-aws-data-lake-banner-image-1.png)
Business Intelligence & Analytics Platform on AWS Data Lake
Banking & Financial ServicesBusiness Impacts
Reduced Response Time by 90%
Reduced the Execution Time for Faster Results
Reduced Cost with Serverless Configuration
Customer Key Facts
- Location : New Jersey
- Industry : Banking & Financial Services
Problem Context
Prosight is a US-based specialty insurance provider which had a Business Intelligence (BI) Platform widely used by the entire organization to visualize data and make data-driven decisions.
Specifically, there was one key report that provided the finance team the ability to slice and dice premium and loss data across multiple dimensions and timeframes. This report was developed in the client’s BI platform utilizing their on-premise SQL Server database
Increasing data volumes resulted in poor response times, impacting the performance of these financial dashboards, especially during peak hours. Even during non-peak periods, the performance was average with higher latency and unoptimized storage.
Challenges
- Storage format had to be shifted from CSV to Parquet for data compression
- Visualization of business insights had to be delivered for analysis of key metrics
Rearchitected the Centralized Data Source Primarily Used by Finance for Analysis of Premium and Loss Data
Solution
Quantiphi helped the client by building a serverless and scalable data source using AWS services.
Result
- The BI platform now utilizes the new scalable data source not just for financial reports that were originally in-scope, but also supporting multiple additional insights and analytics for other departments in the organization, enabling centralized and informed decision making
- Trends and patterns generated from the data source helps business users forecast and predict business growth without any latencies, enabling a great user experience
- The ability to reuse the new data source eliminates the need for building multiple redundant sources and ensures consistency in reported data
- The rearchitected source improves scalability, response times, resource utilization and reduces expenses