Leveraging AI for Metadata Tagging for Enterprise Data Catalog of CDC
Description
Manual metadata tagging for datasets in the CDC Enterprise Data Catalog is inconsistent, incomplete, and time-consuming. The AI automates and standardizes metadata tagging, improving catalog usability and reducing manual effort.
Detailed example
The AI generates suggested metadata fields (tags) for each dataset based on existing metadata, which are then used by staff to improve dataset discovery and relevance in the enterprise data catalog.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The AI increases the speed and consistency of metadata tagging, making the data catalog more usable for CDC staff. This reduces manual effort, improves the completeness and standardization of metadata, and helps staff more efficiently find and use relevant datasets.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Enterprise Data Catalog metadata fields