How Indegene is modernizing and scaling enterprise data quality in life sciences
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Indegene has developed a generative AI-powered Data Quality Agent within its NEXT Unified Data Platform to address the challenges of scaling data quality in the life sciences industry. This agent utilizes Amazon Bedrock and Retrieval Augmented Generation (RAG) to create context-aware validation rules by combining internal enterprise knowledge with external reference standards. By analyzing metadata, profiling results, and anomaly detection, the system generates technical and business-specific SQL and Python code for data integrity checks. The solution significantly reduces manual effort, cutting rule definition time from approximately 30 hours to just 4 hours per dataset, representing an 85% efficiency gain. It maintains strict governance through a human-in-the-loop workflow where data stewards review and approve high-confidence recommendations before execution. Key features include automated anomaly detection, severity classification based on business impact, and seamless integration into existing ETL pipelines. This approach helps life sciences organizations manage complex, regulated data ecosystems while ensuring compliance and trustworthiness for enterprise AI applications.
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