How Indegene is modernizing and scaling enterprise data quality in life sciences

From the source

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.