Success Story.

Modern Utility, Lasting Impact: Transforming SAS to Scalable Python Frameworks

A leading North American insurance powerhouse, the client has long been trusted in property, casualty, and commercial lines. With decades of institutional knowledge and a complex data ecosystem, they had built an aging, robust system. At the core of their operations sat a set of legacy SAS utilities that, while functional, were increasingly inflexible and costly. The client sought to transform this system by migrating to a modern, cloud-based framework to keep pace with modern demands.

Engagement Snapshot

Industry: Insurance

Region: North America

Project Scope: Migration and modernization of legacy SAS enterprise utilities into a consolidated Python and PySpark enterprise utility; cloud migration and on-premise deployment; performance optimization; comprehensive documentation and testing.

Key Technologies: Python, PySpark, Cloud Computing, Visualization Dashboards, Advanced Statistical Libraries, Unit Testing Frameworks

Business Needs

The client’s objectives were both strategic and technical. At the core, they aimed to simplify and modernize a critical set of SAS-based tools that had become increasingly cumbersome and costly to maintain. Their key requirements included:

Replacing legacy SAS utilities with modern, Python and PySpark-based systems.
Refactoring and optimizing tools for improved performance and scalability.
Reducing reliance on SAS licensing and infrastructure.
Consolidating multiple standalone SAS tools into a single, enterprise-wide utility.
Introducing cloud capabilities to overcome the limitations of on-premise environments.
Establishing comprehensive documentation and architectural standards.
Ensuring high code quality through rigorous unit testing and full coverage.

These needs pointed to one overarching goal: to create a unified, future-ready utility that would scale with the business and support smarter, faster decision-making.

Solution

YASH approached the engagement as a full-service transformation. Here’s how we delivered:

Migrated multiple legacy SAS utilities into a scalable enterprise tool built with Python and PySpark.
Ensured the utility was generic enough to support a variety of business lines across the organization.
Developed advanced dashboards to surface insights that were previously hard to access.
Enabled flexible deployment options—cloud-based or on-premise—depending on team needs.
Aligned the new codebase with the client’s enterprise architecture standards to ensure smooth integration.
Produced detailed architecture diagrams and documentation to make maintenance and scaling easy.
Integrated powerful statistical libraries that delivered a 20% performance boost.
Enforced strict code quality and testing standards, achieving 100% test coverage.

This wasn’t a lift-and-shift. It was a full-scale reimagining built for longevity, speed, and insight.

Business Benefits

The transformation delivered clear, measurable outcomes:

Success Story.

Modern Utility, Lasting Impact: Transforming SAS to Scalable Python Frameworks

Business Needs

Solution

Business Benefits

Significant cost savings from eliminating SAS licensing.

A consolidated utility that replaced multiple standalone tools, simplifying operations.

Improved data insights through modern, interactive dashboards.

~20% performance improvements over previous SAS implementations.

Flexible deployment—cloud or on-premise—based on internal needs.

Stronger scalability and maintainability through modern engineering.

Empowered internal teams with extensive documentation and knowledge transfer.

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