Automated Data Normalization for Institutional Records

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 aims to automate the cleanup, transformation, and normalization of large institutional datasets that feed into NASA’s enterprise Salesforce platform. Currently, preparing these records requires time-consuming manual work to merge and standardize disparate data sources before they can be reliably used in NASA systems. By leveraging AI and machine learning, this solution will drastically reduce manual effort, improve data accuracy, and ensure a consistent, high-quality institutional dataset that supports external engagement, enterprise reporting, and mission readiness across the agency.

Expected benefits

STMD has identified a strategic requirement to modernize how the agency tracks and manages interactions with academia and industry in order to promote increased collaboration, expand engagement across the innovation ecosystem, and accelerate the maturation of strategic partnerships. By automating this critical data pipeline, STMD is enabling a more connected, data-driven infrastructure that not only supports its own mission but also delivers agency-wide benefits through improved data accuracy, consistency, and accessibility across all enterprise applications within the platform. End users will benefit from a more robust, reliable, and searchable system of record, making it easier to find and associate with the correct organizations during their interactions with NASA.

System outputs

Data transformations

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