Automated 70% of a 2,500-Module SAS Migration for a U.S. Based Consumer Credit Provider with Data Lifter
Automated 70% of a 2,500-Module SAS Migration for a U.S. Based Consumer Credit Provider with Data Lifter
Client Overview
The client is a U.S. based financial services provider serving millions of customers through credit card and merchant acquiring businesses, with nearly $8 billion in banking assets.
Data supports key parts of the business, from credit decisions and reporting to regulatory compliance. As the analytics environment grew, thousands of SAS modules became harder to maintain and scale.
The Growing Pile of SAS Modules
Nearly 2,500 SAS modules sat behind the client’s analytics operations. These programs supported everything from operational reporting to regulatory analytics.
01
Rising Platform Costs
High licensing and operational costs associated with maintaining the SAS environment.
02
Limited Scalability
The legacy platform struggled to scale efficiently as data volumes and analytics demands increased.
03
Talent Availability Risks
Dependence on a shrinking pool of SAS specialists made long-term maintenance challenging.
04
Barriers to Modern Analytics
Integrating cloud data platforms, advanced analytics, and AI workloads was difficult within the existing environment.
05
Complex Migration Effort
Rewriting thousands of SAS modules while preserving business logic and data integrity posed significant risk.
Diagnosed the Gaps Before Designing the Solution
Data Lifter analyzed the existing SAS environment before any code conversion began. It mapped dependencies and business logic to identify migration risks and opportunities for safe automation.
01
Mapped the SAS Landscape
The platform cataloged nearly 2,500 SAS modules and documented their dependencies to understand how data and logic flowed across the environment.
02
Identifying Hidden Business Logic
Many modules contained embedded business rules built over years of development. These rules were analyzed and documented to prevent loss of logic during migration.
03
Detected Migration Risks Early
Data Lifter identified areas where manual conversion could introduce errors or break downstream processes.
04
Prepared the Environment for AI-Assisted Conversion
The required tooling, testing frameworks, and validation mechanisms were established to support the migration. The platform handled SAS module conversion and validation to the target architecture.
An AI-Driven Migration Built for Large Scale SAS
A migration framework was designed using Data Lifter. It combined AI-assisted code conversion with structured validation to preserve business logic and data accuracy.
Data Lifter MCP & Agent
Data Lifter automated the conversion of legacy SAS modules to Python and supported the migration of data workloads to Snowflake. It managed module dependencies throughout the conversion process, which reduced manual effort and made large-scale legacy migration easier to manage.
Parallel Validation Framework
The SAS and Python versions were run in parallel during migration. Their outputs were automatically compared to catch differences in logic or results and fix them before production.
A Three-Phase Implementation Framework
Each phase moved the migration forward by executing it at scale and validating the final production rollout.
Assessment and Tooling Setup
Data Lifter assessed the SAS modules and mapped their dependencies to define the migration scope. Its agentic architecture was configured with Claude Code for AI-assisted conversion. A 10-module pilot validated the approach before the migration scaled.
Code Conversion
The 2,500 SAS modules were divided into 34 tranches and processed through four parallel PODs. Data Lifter handled the SAS-to-Python conversion and module dependencies. Automated testing compared the converted code against the SAS baseline before each tranche moved forward.
Final Integration and Cutover
Insured customers often depended on operational teams for claim status updates. Without a connected process, maintaining consistent communication across claim stages became increasingly difficult.
Structured Execution Model for Migrating 2,500 SAS Modules
Achieved End-to-End Legacy SAS Modernization
Legacy SAS workloads now run on a scalable analytics foundation, with the underlying business logic and reliability intact.
Accelerated Migration
AI-assisted conversion automated nearly 70% of the code translation process.
Higher Productivity
The conversion increased productivity to ~20,000 LOC per week, compared to ~6,500 LOC per week with manual migration.
Functional Parity
Parallel validation ensured functional parity between SAS and Python outputs. Automated comparisons verified accuracy before production cutover.
Scalable Delivery Model
A factory model used four parallel PODs across 34 tranches. This structure maintained consistent throughput and preserved quality controls throughout the migration.
A Modernized Data Analytics Foundation
With the migration complete, the client now runs critical analytics workloads on a modern Python and Snowflake architecture. The new platform supports high-volume data processing and removes dependency on legacy SAS systems.
Teams can now build and deploy analytics workflows faster while maintaining the business logic and reliability the organization depends on. Data Lifter created a foundation for faster analytics development with greater adaptability.