Approximate String Matching (aka fuzzy matching) to Standardize Data
Description
Entities (producers, commodities, facilities, etc) use different names in our data systems, making accurate analysis by entity impossible without matching and resolving them.
Detailed example
A model is used to match variations of names in Plant Protection and Quarantine (PPQ) program data and apply standardized producer and commodity names. This results in clean, standardized data through an automated workflow.
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
Benefits include reduced labor hours compared to manual data cleaning, makes near-real-time reporting possible, and increases data accuracy which enables program managers to conduct efficient policy enforcement and program monitoring.
Controls / human review
ATO: No; PIA: Not published
Data needed
Entity names are stored within Plant Protection and Quarantine (PPQ) data systems, which include commodity imports, inspections, pest interceptions, and Emergency Action Notifications.