Automated Data Normalization for Institutional Records
Description
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.
Detailed example
Data transformations
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 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.
Controls / human review
ATO: Not reported; PIA: Not published