Machine Learning for Entity Matching

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Service Delivery

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

Classifications/Predictions of matching records

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported