Federal Data: Congressional Action Needed to Improve Interoperability of Award and Payment Eligibility Data
Report by the US Government Accountability Office: “Agencies can use more than 100 federal data sources—or a combination of them—to verify if recipients meet the eligibility criteria for federal programs throughout the award life cycle (which includes pre-award screening, post-award monitoring, and payment validation). As of September 2025, these included 28 data sources in the Do Not Pay working system (DNP) or designated for inclusion in DNP. However, weaknesses in data interoperability may hinder agencies’ ability to efficiently determine award and payment eligibility. Data interoperability is the ability to share and disseminate standardized data in a way that is efficient, consistent, and accessible across different systems and users, for which high-quality data are essential. Without it, the risk of improper awards or payments increases, and the potential use of artificial intelligence and advanced analytics to assist agencies in making eligibility determinations is limited. GAO found that, for more than 30 years, several laws and guidance have established general requirements related to data interoperability but have not established specific requirements for enforcing interoperability, such as for recipient eligibility data, throughout the federal government. Many of the data sources GAO identified, including those in DNP, were created to comply with legal requirements or to manage specific federal programs—not to support eligibility determinations for other agencies. GAO also found a variety of obstacles and challenges that can affect the interoperability of the nine selected data sources that agencies may use for eligibility determinations (see figure). Summary Comparison of Key Elements GAO Assessed to Eligibility Data Interoperability Needs and Observations GAO also found that insufficient or improperly documented validation rules contributed to data quality issues. All nine selected data sources had data quality issues (e.g., missing, invalid, and duplicate data), and seven data sources had inconsistences between them, such as overlap in mutually exclusive data. These data quality issues undermine data reliability and interoperability for agencies seeking to make eligibility determinations…(More)”.
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