Conventional data validation programs often fail because technical teams focus solely on structural flaws like null values, leaving contextual business rules buried in disconnected glossaries and documentation. Indegene addresses this bottleneck by deploying a large language model-powered agent that translates domain context into ready-to-run SQL and Python validation scripts. Operating with centralized human oversight, the system minimizes manual engineering overhead spent juggling spreadsheets and catalogs. Consequently, life sciences organizations can onboard datasets more rapidly to support essential business decisions.
Key points
The automated tool bridges the gap between basic engineering checks and complex domain re…: The automated tool bridges the gap between basic engineering checks and complex domain requirements like prescription activity validation.
Centralized, human-in-the-loop governance guarantees that AI-generated quality checks ali…: Centralized, human-in-the-loop governance guarantees that AI-generated quality checks align with established enterprise standards.
Streamlining dataset onboarding and rule generation frees technical teams to better suppo…: Streamlining dataset onboarding and rule generation frees technical teams to better support commercial, medical, and market access operations.
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Artificial Intelligence
How Indegene is modernizing and scaling enterprise data quality in life sciences
This post is co-written with Shravan K S from Indegene Limited.
Enterprise AI is creating new opportunities for life sciences organizations to improve decision-making across commercial, medical, market access, and other business functions. But the quality of every AI model, dashboard, and business insight depends on one thing: trusted data. As enterprise data grows across multiple platforms, pipelines, and business domains, maintaining consistent data quality has become increasingly difficult. Manual data quality rule creation, fragmented governance, and evolving business definitions make it challenging to scale data quality across the enterprise.
This post explores how Indegene built a production-grade, large language model (LLM) powered Data Quality Agent within its NEXT Unified Data Platform (NEXT UDP) on AWS. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python code under centralized, human-in-the-loop governance. Together, these capabilities help organizations improve data quality at enterprise scale while reducing the effort required to build and maintain data quality programs.
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