Automating Data Stewardship for Temporary Account Reconciliation

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 focuses on automating the manual review and verification process for temporary “write-in” accounts submitted by external users within NASA’s agency-wide enterprise Salesforce platform. When users cannot find their organization in the existing institutional database, they submit temporary account records that require data stewards to manually research, validate, and reconcile each entry—often spending up to 50 hours per week. By leveraging AI-driven entity matching, web verification, and metadata enrichment, this solution will significantly reduce manual effort, accelerate the onboarding of new organizations, and improve data accuracy. Automating this process enhances scalability, ensures compliance with data governance policies, and improves the end-user experience across all applications using the enterprise platform.

Expected benefits

Given the large number of individuals and institutions that show interest in collaborating with NASA, we must ensure that records associated with specific entities remain clean, accurate, and traceable in order to enhance our ability to manage partnerships, track engagement trends, and support strategic outreach across the agency. STMD is championing this automation effort to streamline the reconciliation of temporary accounts, which is essential for maintaining the integrity of NASA’s enterprise-wide contact and organization data. This work not only supports STMD’s mission to accelerate the development and adoption of transformative space technologies through broader collaboration but also strengthens the agency’s ability to scale engagement efficiently and responsibly across academia, industry, and government.

System outputs

Deduplication

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