AI-powered metadata correction and harmonization

As data collection and data generation accelerate, the gap between our ability to produce raw data and our capacity to standardize it continues to widen. Without automation, this gap becomes a critical bottleneck that delays analysis, complicates interpretation, and limits the global value of shared datasets.

Metadata harmonization (standardizing labels, identifiers, and formats so datasets from different sources can work together) remains largely manual. AI-powered metadata correction and harmonization offers a way forward, transforming metadata management from a time-consuming responsibility into a process that scales with your data volume and supports open science rather than obstructing it.

In this post, we demonstrate how AI-powered metadata correction works in practice, explore two implementation approaches (from human-in-the-loop validation to fully autonomous agent-driven workflows), and provide governance considerations for deploying these solutions in your organization.

To address this challenge, we developed a centralized metadata correction and harmonization workflow built on AWS, designed to support consistency, interoperability, and accuracy across disparate metadata sources. The system uses Amazon Bedrock for large language model (LLM)-powered schema alignment and correction recommendations, Amazon Simple Storage Service (Amazon S3) for schema and result storage, Amazon DynamoDB for job tracking, Amazon Cognito for authentication, and Amazon Elastic Container Service (Amazon ECS) for compute. The workflow includes a harmonization package that aligns metadata schemas, validates data integrity, and generates correction recommendations.