AI-powered metadata correction and harmonization

As data generation accelerates, the challenge of standardizing metadata across diverse datasets grows. Manual processes are insufficient at scale, prompting the development of AI-powered solutions for metadata correction and harmonization. These systems leverage large language models (LLMs) and cloud infrastructure to automate schema alignment, validate data integrity, and generate correction recommendations.

The workflow begins with users uploading metadata files, which are then validated through parallel streams: schema alignment verifies structural consistency, while field validation checks individual data points against predefined rules. When issues are identified, targeted recommendations are generated, allowing human reviewers to approve or modify changes, thus maintaining control and domain expertise.

Schema alignment benefits from semantic understanding provided by LLMs, which recognize industry-specific synonyms and infer relationships beyond simple string matching. Field validation employs rule-based checks, including pattern matching and controlled vocabularies, to ensure data compliance. Recommendations are produced through a layered approach combining embedding similarity, contextual inference, fuzzy matching, and fallback to LLM reasoning for complex cases.

Two implementation modes are described: a human-in-the-loop system that accelerates manual review and an autonomous agent-driven workflow that performs corrections with minimal human oversight. Both approaches are supported by a cloud-native architecture on AWS, utilizing services like Amazon Bedrock, S3, DynamoDB, ECS, and Cognito.

Governance considerations emphasize data integrity, change tracking, architecture policies, responsible AI practices, and security measures. Proper controls ensure that metadata modifications are transparent, reversible, and compliant with organizational standards.

This scalable, modular system aims to reduce the manual burden of data standardization, enabling more efficient collaboration and open science. Organizations can start with supervised workflows and gradually adopt more autonomous processes as trust in AI recommendations grows.