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The customer is a leading insurance reseller in India, offering a comprehensive portfolio that includes life, health, motor, and general insurance products. Data plays a critical role in their operations—driving regulatory compliance, customer engagement, reporting accuracy, and strategic decision-making. As the business scaled, the organization realized that its legacy on-premises data infrastructure was becoming a bottleneck, limiting agility, scalability, and timely access to insights.

The Challenge: With growing data volumes and increasing reporting demands, the customer faced several operational constraints:
  • Disconnected data systems, making it difficult to generate consolidated and reliable insights
  • Manual and repetitive reporting processes, leading to delays and human errors
  • Lack of real-time monitoring and alerting, impacting operational visibility
  • Inadequate disaster recovery mechanisms, exposing critical data to risk
  • Limited scalability, restricting the ability to respond quickly to business needs
 These challenges directly affected reporting timelines, compliance readiness, and decision-making speed.

Goals & Objectives The customer aimed to modernize its data landscape with the following objectives:
  • Migrate on-premises data to a scalable cloud-based platform
  • Automate data ingestion, transformation, and reporting workflows
  • Enable self-service analytics and dashboards for business users
  • Strengthen security, compliance, and disaster recovery
  • Accelerate business decisions through real-time insights

The Solution A modern cloud-native analytics platform was designed and implemented on Amazon Web Services (AWS) to address the customer’s requirements.Key elements of the solution included:
  • Creation of a centralized data platform with clearly defined raw, curated, and transformed data zones
  • Automated ETL pipelines using AWS Glue for reliable and consistent data processing
  • Migration of on-premises Microsoft SQL Server databases to Amazon Redshift, forming a unified data warehouse
  • Integration of Excel-based datasets stored in Amazon S3 using AWS Glue and Amazon Athena
  • Deployment of Amazon QuickSight dashboards to enable self-service analytics for business stakeholders
  • Implementation of real-time monitoring, alerting, and cross-region disaster recovery
 Enforcement of encryption and IAM-based access controls to ensure secure data handling

Implementation Highlights 
  • Migrated on-premises MSSQL databases to Amazon Redshift
  • Connected S3-hosted Excel files via AWS Glue and Athena
  • Consolidated structured and semi-structured data into a single analytics layer
  • Designed interactive dashboards on Amazon QuickSight for key business users
  • Enabled continuous monitoring, alerts, and automated backups to support resilience and compliance

AWS Services Used 
  • Amazon Redshift
  • Amazon Simple Storage Service (S3)
  • AWS Glue
  • Amazon Athena
  • Amazon QuickSight
  • AWS Identity and Access Management (IAM)
  • AWS monitoring and disaster recovery services

The Result

The AWS-based analytics transformation delivered clear and measurable outcomes: 
  • 80% reduction in manual reporting effort through automation
  • Faster decision-making enabled by real-time dashboards and insights
  • Improved compliance and resilience with robust DR mechanisms in place
  • Secure, scalable, and future-ready data platform to support business growth
  • Significant improvement in operational efficiency and reporting accuracy

- Author Parbat Singh – AVP, Cloud and New Technology.

Outcome By modernizing its data and analytics infrastructure on AWS, the insurance reseller transformed reporting from a time-consuming operational task into a strategic, insight-driven capability—empowering teams, improving compliance, and enabling faster, smarter decisions across the organization.

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