Efficient Digital Transformation: From Legacy to Leading Edge

One of the world’s largest pharmaceutical giant set out on an ambitious digital transformation journey. Legacy ETL systems, while foundational, are rigid and costly. Upgrading to a modern, automated framework ensures scalability, agility, and reduced costs—critical in a data-driven world. 

However, migrating from the legacy system to new tools was both a cumbersome and high-cost operation, with a high risk of data corruption if not managed well. The off-the-shelf solutions were either lacking in scale or required extensive integration processes, adding to the challenges instead of resolving the situation at hand.

MResult stepped in with a novel Gen AI led solution to build a platform agnostic custom tool that enabled data migration at scale. The framework helped in replacing the legacy Informatica PowerCenter with a modern, scalable ETL (Extract, Transform, Load) framework in Python and PySpark. This enhanced data processing capabilities, ensuring seamless operations, faster drug development, and unparalleled efficiency—while keeping costs under control. 

Managing Scale: Fueling the Pharma Engine with Data at Scale

The pharmaceutical industry generates massive volumes of data from research, clinical trials, manufacturing, and distribution. Efficiently managing this data is critical for compliance, operational optimization, and driving innovation. ETL processes, which transform raw data into actionable insights, are the backbone of this ecosystem. However, the client’s legacy systems were holding them back. Some of the challenges were: 

The Challenge: Cracking the Legacy Code

  • Complex Web of Workflows: With numerous workflows and transformation logic embedded in the existing system, the dependencies were intricate and hard to unravel. 
  • Scaling Stumbling Blocks: The legacy tool struggled with increasing data volumes, leading to bottlenecks and inefficiencies. 
  • Skyrocketing Maintenance Costs: Maintaining the old ETL processes drained time and resources (Human and Financial. 
  • Manual Migration Nightmare: Migrating thousands of workflows manually wasn’t an option due to its sheer complexity and size. 
  • Complex off-the-shelf Solutions: The available solutions in the market were too expensive and required extensive integration. Also, they were not built to scale to meet evolving needs.

The Solution: Automation Meets Intelligence

MResult experts designed a cutting-edge Migration Automation Framework. This innovative approach created a GenAI-based workflow with custom coding to overcome migration hurdles. Here’s how it rewrote the rules with AI at its core: 

  • Intelligent Parsing of XML: The system decoded and extracted transformation logic from Informatica PowerCenter XML files. 
  • Graph/JSON Visualization: Dependencies were mapped out as graphs and JSON structures, allowing a highly reliable contextual source for the GenAI models. 
  • Automated Code Generation: GenAI workflow extracted dependencies and used it to recreate the logic in terms of Python/PySpark code. 
  • Agent Architecture: A Mixture of Agents (MoA) architecture was adopted to ensure iterative improvements, blending smaller, context generating models with powerful aggregators which use the context generated by the smaller models as inputs to generate higher quality outputs. 
  • Sparse Priming Representation (SPR): This technique mimics a human expert and was used to further strengthen the MoA architecture to capture the subtle nuances from the extracted source code including its dependencies to enhance code generation accuracy and refinements in the final steps. 
    • Reflections and Styling Guidelines: Reflections of the output is a best practice in GenAI workflows which got emulated even here as multiple steps of reinforcement and followed by that styling guidelines were added to add annotations to the generated code block to allow the human developer to understand it better for debugging , accuracy enhancements and further optimizations or even further downstream GenAI workflows, making it highly scalable 

The Framework: Designing Platform Agnostic Utility

MResult experts team went beyond the current environment of Informatica PowerCenter migration and built a framework that can be deployed on any platform with minor customizations. This makes data migration at scale future proof and mitigates complexities with GenAI powered solutions, at pace with business transformation strategies. Here’s how the framework was designed:

  • Assessment and Planning: Mapping out the legacy environment, including dependencies and transformation logic. 
  • Intelligent Parsing: Developing algorithms to extract complex XML-based transformation logic along with connections to source and target tables by virtue of the deep insight developed in the process allowing them to tap into the right object. 
  • Code Generation: Employing GenAI for Python/PySpark code creation. 
  • Data Structuring: Structuring  workflows as  graphs/JSON for clarity and enhancement and most importantly allowing the GenAI to models to handle the issues of Lost in the Middle in the usual RAG based architectures 
  • Iterative Refinement: Deploying MoA and SPR to strengthen the overall workflow and bring it closer to being an expert in code generation bestowed with the ability to optimize and enhance the generated code in the future. 
  • Validation and Testing: Ensuring accuracy and efficiency in a PySpark environment 

Long-Term Gains: The Dual Impact of an Innovative Data Migration Framework

  • Automated migration processes reduced project timelines by 80%, enabling higher time-to-value and freeing resources for strategic growth initiatives. 
  • The scalable framework seamlessly handled massive datasets, ensuring the system could grow with a projected 40% annual increase in data volume. 
  • Operational costs were reduced by 30% through optimized maintenance processes, translating into significant long-term savings and productivity gains. 
  • ETL performance improved by 50%, accelerating data processing and enhancing analytical capabilities for faster, more informed decision-making. 
  • Enhanced data quality and integrity ensured 99.9% accuracy post-migration, aligning with industry benchmarks and reducing downstream errors. 
  • Streamlined data management accelerated drug development cycles, enabling up to 25% faster innovation from lab to market. 
  • A modernized infrastructure delivered a competitive edge, positioning the company for sustained leadership in a $1.4 trillion pharmaceutical industry.   

Out-of-Box Approach: Delivering Scalable and Reusable Solutions with GenAI

MResult experts empowered the client to leverage GenAI powered automated solutions to transform their ETL processes, driving efficiency, scalability, and innovation. By replacing outdated systems with innovative frameworks, the client not only streamlined operations but also positioned themselves for a future defined by efficiency, agility, and innovation