Client
One of Australia’s Oldest Banks
The client, one of the oldest and leading banks in Australia, has been a cornerstone of the financial sector for decades. With a rich history of trust and service, the bank had built a robust analytics infrastructure over the years, primarily relying on SAS (Statistical Analysis System) for its data processing and analytics workloads. But as the banking landscape evolved, so did the need for modernization. The bank faced mounting challenges with its legacy SAS infrastructure, which was limiting scalability, driving up costs, and slowing innovation in an increasingly competitive market.
Challenge
Navigating the Roadblocks to Transformation
The Weight of Legacy Systems
The bank’s extensive SAS estate was deeply ingrained in its business processes, making it both an asset and a liability. While it had served well for decades, the system was increasingly becoming a bottleneck due to:
- High Licensing and Operational Costs: The substantial expenses associated with maintaining legacy SAS licenses and infrastructure were eating into the bank’s IT budget.
- Limited Scalability: As the bank’s data volume grew exponentially, scaling the SAS systems to meet modern demands became an arduous and costly task.
- Slow Processing: The aging infrastructure struggled to keep pace with the fast-changing needs of data analytics, leading to inefficiencies and delays.
Operational Complexities and Uncertainty
The bank’s analytics workloads were extensive, with complex SAS scripts and constructs scattered across its operations. This led to:
- Uncertainty in Transformation: A lack of clarity around the scope and potential roadblocks of the modernization journey.
- Repetitive and Non-Productive Scripts: Many scripts were outdated or redundant, adding unnecessary weight to the system.
- Manual Efforts: The reliance on manual processes for validation and reconciliation added to inefficiencies, leaving room for error and delays.
With these challenges looming large, the bank needed a partner who could not only modernize its analytics infrastructure but also guide it seamlessly through the complexities of transformation.
Solution
Hexaware’s AI-Driven Modernization Journey with Amaze®
Hexaware stepped in with its cutting-edge solution, leveraging Amaze®, an AI-driven agentic code conversion engine designed to modernize and optimize legacy systems. With a focus on scalability, efficiency, and cost-effectiveness, Hexaware delivered a solution that redefined the bank’s approach to analytics.
What did Hexaware do?
- Comprehensive Discovery & Assessment
Hexaware began with a detailed discovery and assessment phase to reduce transformation uncertainty by nearly 80%. Using an automated reporting mechanism, the team identified reusable SAS macros and potential conversion candidates, ensuring a streamlined and focused approach.
- AI-Led SAS Rationalization
Hexaware employed AI to rationalize the bank’s extensive SAS estate. Non-productive scripts were automatically identified and removed from the scope, drastically reducing the workload and enabling efficient conversion.
- Automated Code Conversion
Complex SAS constructs were transformed into analytics-ready Snowpark outcomes using Hexaware’s proactive assessment engine. Automated dataset comparisons between SAS and Snowpark ensured accuracy, detecting mismatches at a granular level.
- Seamless Cloud Integration
The solution culminated in the deployment of Snowpark notebooks on a scalable, cost-effective cloud infrastructure. This ensured the bank was future-ready with a modern, cloud-native analytics framework.
Implementation: A Step-by-Step Transformation
Hexaware executed the solution in phases, ensuring minimal disruption to the bank’s operations:
- Identified and removed redundant scripts, reducing scope and complexity.
- Converted over 150,000 lines of SAS code into reusable Snowpark constructs.
- Automated validation processes to ensure seamless migration with reduced manual intervention.
Benefits
A Future-Ready Analytics Landscape
- 95% Elimination of Repetitive Scripts
By removing redundant and non-productive scripts, Hexaware helped the bank streamline its analytics workloads, reducing unnecessary tasks and improving system efficiency.
- 90% Reduction in Manual Effort
Manual validation and reconciliation efforts were replaced with automated processes, saving time, reducing errors, and freeing up resources for strategic initiatives.
- Cost Savings from Cloud Integration
The deployment of Python constructs on a cloud infrastructure significantly reduced operational costs while enabling scalability.
- Enhanced Processing Speeds
With the move to Python and Snowpark, the bank achieved faster processing times, enabling quicker insights and more agile decision-making.
- Scalability for Future Growth
The cloud-based infrastructure provided a scalable foundation, empowering the bank to handle growing data volumes and increasing analytical demands seamlessly.
Summary
Redefining Analytics for Long-Term Success
Today, the client stands at the forefront of banking innovation, armed with a modern, scalable analytics framework ready to meet tomorrow’s demands. Hexaware’s AI-driven SAS modernization has not only addressed the bank’s immediate challenges but has also empowered it with the tools to thrive in a data-driven world. By eliminating inefficiencies, reducing costs, and enabling scalable, cloud-based analytics, Hexaware has helped the bank transform its legacy infrastructure into a future-ready powerhouse. The journey doesn’t end here—Hexaware continues to support the client with ongoing optimization and innovation, ensuring the bank remains competitive in an ever-changing industry.
Transform Your Legacy Analytics Today
Ready to modernize your analytics infrastructure like one of Australia’s oldest banks? Discover how Hexaware’s AI-powered Amaze® can help you reduce costs, enhance scalability, and unlock faster insights.
