Machine Learning for Entity Matching
Description
This AI use case addresses a critical data quality challenge within NASA’s enterprise Salesforce platform by enabling NASA to accurately merge extremely large institutional datasets from multiple sources into a single, unified system, while identifying and preventing duplicate account entries that result from inconsistencies in naming, formatting, and metadata. With these inconsistencies across sources, traditional matching methods are insufficient to maintain a clean and reliable master list of organizations. By implementing AI-assisted entity matching and de-duplication, NASA can ensure accurate, non-redundant records that enhance reporting, support seamless user experiences, and enable the agency to scale its engagement infrastructure without compromising data integrity.
Detailed example
Classifications/Predictions of matching records
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
STMD is advancing this initiative in support of its broader effort to modernize how NASA collaborates with academia, industry, and government entities. Given the large number of individuals and organizations seeking to engage with NASA, it is essential that institutional records remain clean, traceable, and well-governed. This use case directly enhances STMD’s ability to manage an authoritative system of record that supports outreach, partnership tracking, and strategic engagement. By implementing AI-driven matching and de-duplication, STMD is not only enabling faster and more reliable onboarding of new institutional data, but also delivering enterprise-wide value by improving data integrity across all applications built on the shared Salesforce platform. These improvements will ensure that external users experience a seamless, accurate interface when associating with their organization—while internal teams gain more trustworthy data for programmatic planning, performance tracking, and cross-agency collaboration.
Controls / human review
ATO: Not reported; PIA: Not published