Federal Data: Congressional Action Needed to Improve Interoperability of Award and Payment Eligibility Data
Summary
A Government Accountability Office report to congressional requesters, GAO-26-107466, dated June 2026, titled Federal Data: Congressional Action Needed to Improve Interoperability of Award and Payment Eligibility Data. GAO reports that agencies can use more than 100 federal data sources to verify eligibility, including 28 in or designated for the Do Not Pay working system as of September 2025. All nine selected data sources had data quality issues, and for SAM Exclusion records with a unique entity identifier GAO identified 2,074 awards to listed recipients, stating that such matches by themselves do not indicate improper awards. GAO recommends that Congress consider giving a single agency, such as Treasury, explicit authority to lead data standards for recipient eligibility data.
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“Federal Data: Congressional Action Needed to Improve Interoperability of Award and Payment Eligibility Data.”
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United States Government Accountability Office
Report to Congressional Requesters
FEDERAL DATA
June 2026
Congressional Action
Needed to Improve
Interoperability of
Award and Payment
Eligibility Data
GAO-26-107466
FEDERAL DATA
Highlights Congressional Action Needed to Improve Interoperability of
Award and Payment Eligibility Data
GAO-26-107466 June 2026
A report to congressional requesters
Contact: Paula M. Rascona at rasconap@gao.gov
What GAO Found Why GAO Did This Study
Agencies can use more than 100 federal data sources—or a combination of Government agencies are responsible
them—to verify if recipients meet the eligibility criteria for federal programs for ensuring that data, including those
throughout the award life cycle (which includes pre-award screening, post-award needed to determine whether entities
monitoring, and payment validation). As of September 2025, these included 28 are eligible to receive federal awards
data sources in the Do Not Pay working system (DNP) or designated for and funds, are interoperable and
inclusion in DNP. However, weaknesses in data interoperability may hinder reliable. Having interoperable data
agencies’ ability to efficiently determine award and payment eligibility. among agencies and data sources is
crucial to improving the federal
Data interoperability is the ability to share and disseminate standardized data in government’s efforts to detect and
a way that is efficient, consistent, and accessible across different systems and prevent improper payments. The
users, for which high-quality data are essential. Without it, the risk of improper Payment Integrity Information Act of
awards or payments increases, and the potential use of artificial intelligence and 2019 requires executive agencies to
advanced analytics to assist agencies in making eligibility determinations is take actions to reduce improper
limited. payments, such as using DNP to
ensure that they make awards and
GAO found that, for more than 30 years, several laws and guidance have payments only to eligible recipients.
established general requirements related to data interoperability but have not
established specific requirements for enforcing interoperability, such as for GAO was asked to review how the
government can better leverage
recipient eligibility data, throughout the federal government. Many of the data
USAspending.gov and other data
sources GAO identified, including those in DNP, were created to comply with
sources to help enhance monitoring of
legal requirements or to manage specific federal programs—not to support federal spending and potential fraud,
eligibility determinations for other agencies. waste, and abuse. This report describes
GAO also found a variety of obstacles and challenges that can affect the (1) federal data sources agencies may
interoperability of the nine selected data sources that agencies may use for use to verify award recipient’s eligibility,
eligibility determinations (see figure). (2) the extent to which selected
eligibility data sources are
Summary Comparison of Key Elements GAO Assessed to Eligibility Data interoperable, and (3) the extent to
Interoperability Needs and Observations which eligibility data can be matched
with USAspending.gov post-award
information to analyze potentially
ineligible recipients.
To conduct this review, GAO
• reviewed laws, regulations, OMB
guidance, and relevant federal
agencies’ websites and
documentation to identify federal
data sources that agencies can use
to determine award recipients’
eligibility;
• reviewed laws, regulations, policies,
and OMB guidance related to data
interoperability to identify
requirements;
• judgmentally selected nine eligibility
data sources that were publicly
available, in DNP or designated for
GAO also found that insufficient or improperly documented validation rules inclusion in DNP, and included
contributed to data quality issues. All nine selected data sources had data quality information about entities;
United States Government Accountability Office
issues (e.g., missing, invalid, and duplicate data), and seven data sources had • reviewed agencies’ data
inconsistences between them, such as overlap in mutually exclusive data. These dictionaries and documentation of
data quality issues undermine data reliability and interoperability for agencies validation processes about the
seeking to make eligibility determinations. selected eligibility data sources for
consistency with interoperability
To determine the extent to which eligibility and award data could be linked to help practices GAO identified;
agencies identify whether potentially ineligible entities had received federal • tested data for fiscal years 2023
awards, GAO partially linked two data sources—System for Award Management and 2024 for data quality issues,
(SAM) entity information and SAM Exclusion records—with USAspending.gov such as missing and invalid values,
awards based on the unique entity identifiers (UEI). Most USAspending.gov consistency, and comparability
award recipient UEIs could be linked to SAM entity information. However, most based on specifications established
SAM Exclusion records, which identify parties excluded from receiving federal by the data owners and GAO’s
benefits and awards, such as contracts, did not have a UEI because this data professional judgment;
source does not always require them. For the SAM Exclusion records with a UEI, • interviewed officials at Treasury,
GAO identified 2,074 awards to recipients that were listed in the data source at the General Services
the time of the transaction. However, these matches by themselves do not Administration, and the Department
indicate that the awards were improper or involved fraud, waste, or abuse. of Health and Human Services’
Making this determination requires specific evaluation of each case. Office of Inspector General
because they own the selected
The inability to fully analyze SAM Exclusions and USAspending.gov data is an data sources; and
example of government-wide issues with data matching for recipient eligibility • linked SAM data sources with
determinations. While analysis based on unique identifiers can support eligibility USAspending.gov award
determinations, such identifiers might not always be required or available. information using UEI.
Improved data interoperability—including standardized data elements and
increased interoperability of data elements, such as names and addresses What GAO Recommends
across data sources and agencies—could enable more comprehensive and
efficient data matching. This would improve the government’s ability to identify GAO recommends that Congress
potentially ineligible recipients. consider assigning a single agency,
such as Treasury, explicit authority to
In addition, several federal agencies and cross-agency groups support best lead, in coordination with the CDO
practices for data management and interoperability. However, there is no data Council and in consultation with the
governance agency designated to establish and enforce mandatory data OMB—and others, as needed—the
interoperability requirements to support recipient eligibility determinations. This development and implementation of
has led to fragmented and inconsistent data management efforts that rely on government-wide data standards and
agencies’ voluntary adoption. interoperability requirements for
recipient eligibility data sources. These
Congress could help improve government-wide data interoperability for recipient sources include relevant financial,
eligibility data by assigning a single agency a lead role in establishing and award, spending, and payment data
implementing data interoperability requirements for recipient eligibility data needed to support eligibility
sources. Based on its role supporting agencies in their efforts to prevent and determinations throughout the award
detect improper payments and operating systems that collect, validate, and use life cycle. In its comments, the Bureau
financial, award, spending, and payment data, the Department of the Treasury of the Fiscal Service agreed with the
could be assigned the explicit authority to establish and implement mandatory findings of GAO’s report and stated
government-wide data standards and interoperability requirements for recipient that, to be successful in setting
eligibility data sources. Treasury could then work with the Chief Data Officer government-wide data standards,
(CDO) Council and the Office of Management and Budget (OMB) to implement Treasury would require clear authority
the requirements. Not having a data governance agency will contribute to to lead standardization related to
unreliable reporting and inefficiencies as agencies attempt to determine recipient eligibility data that are designated for
eligibility, and it will limit the government’s ability to leverage artificial intelligence use in DNP. OMB did not provide
comments.
and advanced analytics to identify and prevent improper awards and payments.
United States Government Accountability Office
Contents
Letter 1
Background 4
Agencies May Use Many Federal Data Sources to Verify Entities’
Eligibility Throughout the Award Life Cycle 11
Data Interoperability Weaknesses May Hinder Agencies’ Ability to
Efficiently Determine Award and Payment Eligibility 14
Comparability of USAspending.gov and SAM Data and Efficient
Identification of Potentially Ineligible Recipients 44
Conclusions 46
Matter for Congressional Consideration 47
Agency Comments 47
Appendix I Objectives, Scope, and Methodology 50
Appendix II Publicly Available File Formats of the Selected Data Sources 57
Appendix III Comments from the Bureau of the Fiscal Service 58
Appendix IV GAO Contact and Staff Acknowledgments 60
Related GAO Products 61
Tables
Table 1: Description of the Do Not Pay Working System Data
Sources GAO Selected 8
Table 2: Laws and an Executive Order with Requirements
Relevant to Data Interoperability for Federal Agencies 16
Table 3: Guidance Relevant to Data Interoperability for Federal
Agencies 18
Table 4: Stakeholders of Federal Data Interoperability That GAO
Identified and Their Management Areas, Roles, and
Responsibilities 20
Page i GAO-26-107466 Federal Data Interoperability
Table 5: GAO Assessment of Data Element Specifications in Data
Dictionaries for the Nine Selected Data Sources 23
Table 6: Examples of Fields Representing Names in the Selected
Federal Data Sources 24
Table 7: Common Unique Identifiers Captured in the Nine
Selected Data Sources 26
Table 8: Data Quality Issues GAO Found by Data Source 29
Table 9: Frequency of Updates and Availability of Historical
Information of the Selected Data Sources 40
Table 10: Summary of USAspending.gov Awards, System for
Award Management (SAM) Exclusions, and SAM Entity
Information Matched Using UEIs, Fiscal Years 2023–2024 44
Table 11: Publicly Available File Formats of the Selected Data
Sources 57
Figure
Figure 1: Examples of the Different File Formats of the Selected
Data Sources 42
Abbreviations
API application programming interface
ARL Automatic Revocation of Exemption List
CDO Chief Data Officer
CFO Act Chief Financial Officers Act of 1990
CSV Comma-Separated Values
Page ii GAO-26-107466 Federal Data Interoperability
DATA Act Digital Accountability and Transparency Act of
2014
DNP Do Not Pay working system
EIN employer identification number
FAC Federal Audit Clearinghouse
FAR Federal Acquisition Regulation
FFATA Federal Funding Accountability and Transparency
Act of 2006
GREAT Act Grant Reporting Efficiency and Agreements
Transparency Act of 2019
GSA General Services Administration
HHS Department of Health and Human Services
IRS Internal Revenue Service
JSON JavaScript Object Notation
LEIE List of Excluded Individuals and Entities
NIEM National Information Exchange Model
NPI National Provider Identifier
OFAC Office of Foreign Assets Control
OIG Office of Inspector General
OMB Office of Management and Budget
PDF Portable Document Format
PIIA Payment Integrity Information Act of 2019
SAM System for Award Management
SDN Specially Designated Nationals and Blocked
Persons
SSA Social Security Administration
UEI unique entity identifier
Uniform Guidance Uniform Administrative Requirements, Cost
Principles, and Audit Requirements for Federal
Awards
UTC Coordinated Universal Time
XML Extensible Markup Language
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Page iii GAO-26-107466 Federal Data Interoperability
Letter
441 G St. N.W.
Washington, DC 20548
June 16, 2026
Congressional Requesters
Accurate and timely eligibility data are essential for ensuring that
agencies only award and pay federal funds to entities that meet
applicable legal and regulatory criteria. This is especially critical in
preventing the misuse of funds, including fraud, waste, and abuse.
Inaccurate eligibility data, such as incomplete records, can lead to
improper payments, including payments to ineligible recipients or to
recipients that cannot be verified.
Quality eligibility data also enables greater transparency, accountability,
and effective government operations, including the potential use of
artificial intelligence to analyze large datasets, identify risks and
anomalies, and support more efficient oversight. The use of eligibility
information to prevent improper federal awards and payments and the
role of USAspending.gov in detecting potentially improper awards are
vital to safeguarding taxpayer dollars.
The Payment Integrity Information Act of 2019 (PIIA) requires executive
agencies to take specific actions to identify and reduce improper
payments. 1 This includes mandatory use of the Do Not Pay working
system (DNP) to ensure that they make awards and payments only to
eligible recipients. 2 DNP includes information from certain databases,
such as those that track debarred, suspended, or excluded parties, that
executive agencies may use to help prevent ineligible entities from
receiving federal awards or funds.
The use of eligibility data is fundamental to maintaining the integrity of the
federal procurement and grants processes. In our prior work evaluating
agencies’ use of DNP, we emphasized the need for better integration of
eligibility checks into award decision-making processes. 3 When agencies
do not effectively use eligibility data, it increases the risk of improper
federal awards and payments, undermining PIIA’s goals. This highlights
1Pub. L. No. 116-117, 134 Stat. 113 (2020), codified at 31 U.S.C. §§ 3351-3358.
231 U.S.C. § 3354.
3GAO, Improper Payments: Strategy and Additional Actions Needed to Help Ensure
Agencies Use the Do Not Pay Working System as Intended, GAO-17-15 (Washington,
D.C.: Oct. 14, 2016).
Page 1 GAO-26-107466 Federal Data Interoperability
the urgent need for agencies to prioritize the quality, accuracy, and
accessibility of eligibility data, ensuring that they consistently apply the
data to verify recipients’ eligibility before they award and pay funds.
USAspending.gov—the government’s official public source for federal
spending information, including federal awards, recipients, obligations,
and outlays—plays a critical role in providing transparent, accessible
information on federal spending. Making this information publicly available
helps enhance oversight, enabling federal agencies, including their
Offices of Inspector General (OIG), and the public to detect potentially
improper awards and payments.
Accurate and compatible eligibility data—data that are standardized and
aligned across systems to enable consistent comparison and use—help
agencies support these award decisions, perform automated and
systemic eligibility checks, and capture relevant spending information. As
such, ensuring that agencies can effectively match eligibility data and
award spending information, to validate that only eligible entities receive
federal funds, is crucial to improving the success of efforts aimed at
detecting and preventing improper payments.
This report is part of a series of reports in response to your request for us
to study how the government can better leverage USAspending.gov and
other data sources to enhance monitoring of federal spending and to help
identify and prevent fraud, waste, and abuse. 4 This report (1) describes
the federal data sources that agencies may use to verify entities’ eligibility
to receive federal awards, (2) determines the extent to which data in the
selected federal eligibility data sources are interoperable, and (3)
determines the extent to which eligibility data can be matched with post-
award data from USAspending.gov to help agencies identify potentially
ineligible recipients of federal awards.
For objective one, we reviewed laws, regulations, Office of Management
and Budget (OMB) guidance, and relevant federal agencies’ websites and
documentation to identify federal data sources agencies may use to
determine recipients’ eligibility for federal awards. We obtained an
understanding of the Department of the Treasury’s DNP by reviewing
related web pages, documentation, and information on integrated data
4GAO, Federal Spending Transparency: Actions Needed to Help Ensure Procurement
Data Quality, GAO-25-107469 (Washington, D.C.: Sept. 25, 2025), and Grants
Management: Recent Guidance Could Enhance Subaward Oversight, GAO-25-107315
(Washington, D.C.: Mar. 26, 2025).
Page 2 GAO-26-107466 Federal Data Interoperability
sources and federal agency participation, and by interviewing agency
officials. We also interviewed OMB staff and officials from federal
agencies that own and manage eligibility data sources or administer DNP,
such as the General Service Administration (GSA), the Department of
Health and Human Services’ (HHS) OIG, and Treasury, to identify and
better understand federal data sources available to agencies.
To address objective two, we judgmentally selected nine data sources to
assess data interoperability. We selected eligibility data sources that were
publicly available, in DNP or designated for inclusion in DNP, and
included information about entities. We also considered whether the data
sources contained information to support eligibility determinations. We
selected (1) GSA’s Federal Audit Clearinghouse (FAC) Single Audit
Reports, (2) GSA’s System for Award Management (SAM) Entity
Registrations, (3) GSA’s SAM Exclusions, (4) HHS OIG’s List of Excluded
Individuals and Entities (LEIE), (5) the Internal Revenue Service’s (IRS)
Automatic Revocation of Exemption List, (6) IRS’s Form 990-N list, (7)
IRS’s Publication 78 list, (8) the Office of Foreign Assets Control’s
(OFAC) Specially Designated Nationals and Blocked Persons (SDN) List,
and (9) OFAC’s Non-SDN Lists. 5
For the selected data sources, we reviewed data dictionaries and
documentation of validation processes for consistency with OMB
guidance and other interoperability practices we identified. These
practices included the U.S. Geological Survey’s guidance on data
dictionaries and metadata and Treasury’s Governmentwide Spending
Data Model documentation and validation rules. We interviewed agency
officials responsible for the selected sources to understand how they
collected, validated, maintained, and monitored their data. In addition, we
performed electronic testing of data for fiscal years 2023 and 2024 (the
latest available at the time of our analyses) for apparent errors in
accuracy and completeness, such as duplicate records, missing or invalid
values, data formatting, and conflicting data relationships. To determine
consistency across data sources, we performed electronic comparison
procedures, where applicable, using exact matching.
5While OFAC’s Non-SDN Lists have not specifically been designated for inclusion in DNP,
they are included in the SAM Exclusions, which were designated and are part of DNP.
Therefore, we included them in our selection of data sources. In this report, the Non-SDN
Lists refer to the lists included in the Non-SDN Consolidated Sanctions List. For the nine
selected data sources, we aimed to work exclusively with publicly accessible data for
fiscal years 2023 and 2024.
Page 3 GAO-26-107466 Federal Data Interoperability
For objective three, we narrowed our selection to the data sources that
Treasury’s DNP categorized as debarment data sources and had the
unique entity identifier (UEI) data element, which USAspending.gov
uses. 6 We selected GSA’s SAM entity information and SAM Exclusions.
We matched selected data from these data sources with relevant
USAspending.gov award data for fiscal years 2023 and 2024 based on
the UEI to identify awards to potentially ineligible recipients. To the extent
possible, we also quantified the number and obligation amounts of
awards that matched. As discussed later in the report, data limitations
prevented us from analyzing the full data set to identify awards to
potentially ineligible recipients using UEIs.
Because we made a nongeneralizable selection of data sources, our
findings for objectives two and three cannot be used to make inferences
about other data sources nor the full population of awards. However, we
determined that the selection of these data sources was appropriate for
our design and objectives, and that the selection would generate valid
and reliable evidence to support our work. We provide additional details
regarding our objectives, scope, and methodology in appendix I.
We conducted this performance audit from March 2024 to June 2026 in
accordance with generally accepted government auditing standards.
Those standards require that we plan and perform the audit to obtain
sufficient, appropriate evidence to provide a reasonable basis for our
findings and conclusions based on our audit objectives. We believe that
the evidence obtained provides a reasonable basis for our findings and
conclusions based on our audit objectives.
Background
Legal Framework and The Federal Funding Accountability and Transparency Act of 2006
Guidance for Federal (FFATA) requires federal awards of more than $25,000 and subawards of
more than $30,000 to be displayed on a publicly accessible and
Award Eligibility and
Payment Integrity
6In February 2026 Treasury revised the DNP website and database categories. For
example, previously Treasury categorized GSA’s SAM Entity Registrations and SAM
Exclusions as debarment. These are currently categorized as Entity and Disallowed,
respectively. The UEI is an identification number that SAM.gov assigns to entities doing
business with the federal government.
Page 4 GAO-26-107466 Federal Data Interoperability
searchable website, which is USAspending.gov. 7 Federal awards
displayed on the website include the following:
• Contracts. Agreements between the federal government and a prime
recipient to provide goods and services for a fee, such as purchasing
equipment or supplies. They include contracts, subcontracts, purchase
orders, task orders, and delivery orders.
• Financial assistance. Federal program, service, or activity that directly
aids organizations; individuals; or state, local, or tribal governments, such
as funding for a lower-income housing program. It includes grants,
subgrants, loans, cooperative agreements, and insurance.
For purposes of this report, we refer to both contracts and federal
financial assistance collectively as “federal awards.” A variety of laws,
regulations, and guidance establish a framework for ensuring the integrity
of award-related funds, with specific legal requirements varying based on
the type of agreement used.
The Federal Acquisition Regulation (FAR), which codifies uniform policies
for executive agencies to acquire supplies and services, governs most
contracts. The FAR, together with individual agencies’ regulations
implementing or supplementing it, provides rules for contractors’
qualifications, monitoring, and oversight. For example, prospective
contractors must meet standards of responsibility in areas such as
financial resources and past performance. 8 It also requires agencies and
contractors to check SAM Exclusions to prevent awarding contracts and
subcontracts to contractors with active exclusion records, such as
contractors that have been debarred or suspended. 9
OMB has issued Uniform Administrative Requirements, Cost Principles,
and Audit Requirements for Federal Awards (Uniform Guidance),
reprinted in 2 C.F.R. part 200, which provides government-wide policy for
executive agencies and nonfederal entities carrying out federal financial
assistance programs. Among other things, it requires agencies and
entities to review eligibility and to monitor activities and reports to ensure
7Pub. L. No. 109-282, 120 Stat. 1186 (2006), codified at 31 U.S.C. § 6101 note; 2 C.F.R.
Part 170. The Digital Accountability and Transparency Act of 2014 amended FFATA. Pub.
L. No. 113-101, 128 Stat. 1146 (2014), codified at 31 U.S.C. § 6101 note.
8FAR § 9.104.
9FAR §§ 9.404, 9.405-2.
Page 5 GAO-26-107466 Federal Data Interoperability
compliance. 10 OMB also provides guidance in part 170 of 2 C.F.R. to
agencies on reporting awards and establishes requirements for recipients’
reporting on subawards to SAM.gov, which is then published on
USAspending.gov.
PIIA requires agencies to manage improper payments by identifying risks,
taking corrective actions, and estimating and reporting on improper
payments in programs they administer. 11 As discussed, PIIA mandates
that agencies use, at a minimum, DNP to ensure that they make awards
and payments only to eligible recipients.
Treasury has a central role in managing payments for the federal
government, with broad authority to disburse funds on behalf of other
agencies and to maintain the financial reporting and accounting system of
the federal government. 12 A 2025 executive order entitled Protecting
America’s Bank Account Against Fraud, Waste, and Abuse instructed
agencies to make greater use of Treasury’s centralized disbursement
system. 13 It also enhanced the requirement for agencies to use DNP as
part of a precertification and pre-award process. Further, the executive
order directed Treasury to minimize administrative barriers to accessing
and using data to prevent fraud and improper payments, such as by using
existing authority to waive requirements for computer matching
agreements. 14
Do Not Pay and Federal The Do Not Pay initiative, which PIIA statutorily authorized, provides
Efforts centralized access to certain databases to verify a recipient’s payment or
award eligibility before issuance. 15 DNP, which Treasury operates and
OMB oversees, provides data matching, data-analytics services, and
102 C.F.R. §§ 200.206, 200.329, 200.332, and 200.505.
1131 U.S.C. § 3352.
1231 U.S.C. §§ 3321, 3513.
13Exec. Order No. 14249, 90 Fed. Reg. 14011 (Mar. 28, 2025).
14A computer matching agreement is a written agreement between a source agency and a
recipient agency. It defines the terms and safeguards for comparing federal records
against other federal records, primarily to establish or verify eligibility for federal benefits.
1531 U.S.C. § 3354. Prior to the enactment of PIIA in 2020, a similar provision in the
Improper Payments Elimination and Recovery Improvement Act of 2012, Pub. L. No. 112-
248, § 5, 126 Stat. 2390, 2392–2393 (2013) (which PIIA repealed), and a 2010
Presidential Memorandum, Enhancing Payment Accuracy Through a “Do Not Pay List”, 75
Fed. Reg. 35,953 (June 18, 2010), provided authorization for DNP.
Page 6 GAO-26-107466 Federal Data Interoperability
support to executive agencies in their efforts to prevent and detect
improper payments. State agencies that administer federal programs may
also use DNP, which is free for all authorized users.
DNP Products and Services Treasury’s Office of Financial Integrity manages DNP and offers a variety
of services and solutions, such as account and entity verification and data
analytics. It provides the following services.
• Do Not Pay Connect. Allows users to connect through a secure
application programming interface for real-time, system-to-system
eligibility screening by conducting single or batch queries. It also provides
users with risk and confidence levels for identifying and making eligibility
determinations.
• Do Not Pay Web Portal. Provides users on-demand access through a
web-based portal to conduct single eligibility searches and review batch
matching results and continuous monitoring alerts. This option can also
generate and export detailed reports.
• Treasury Payment Verification. Automatically screens federal agencies’
disbursements against authoritative data to confirm identity and eligibility,
helping flag ineligible payments and reduce improper payments. While
this provides a critical safeguard, agencies must still conduct prepayment
eligibility checks through DNP Connect and Web Portal. This service is
only available to federal agencies that use Treasury for payment
disbursement.
Participating states can also securely access DNP through program
integrity hubs, such as the HHS’s Administration for Children & Families’
Public Assistance Reporting Information System and the Department of
Labor’s Unemployment Insurance Integrity Center Integrity Data Hub.
These hubs can be used, as applicable, to detect and prevent duplicate
public assistance benefits, verify eligibility, identify deceased payees,
confirm bank account ownership, and prevent unemployment insurance
fraud, among other things.
Data Sources Used by DNP PIIA requires that, at a minimum, agencies must review the following six
databases via DNP before issuing payments and awards: (1) the Social
Security Administration’s (SSA) Death Master File; (2) GSA’s SAM
Exclusions; (3) Treasury Debt Check; (4) the Department of Housing and
Urban Development’s Credit Alert System, also known as the Credit Alert
Page 7 GAO-26-107466 Federal Data Interoperability
Interactive Voice Response System; (5) HHS OIG’s LEIE; and (6) SSA’s
incarcerated individuals list. 16
As of September 2025, OMB had designated 22 additional databases for
inclusion in DNP. Treasury evaluates and integrates the databases into
DNP based on multiple factors, including technical feasibility; whether a
database addresses a relevant root cause of improper payments; data
quality and reliability; the program’s authorities; and data compatibility
with the tool’s matching capabilities, such as presence of a unique
identifier—for example, employer identification number (EIN) or UEI—of a
potential recipient. In June 2025, OMB delegated the authority to
designate data sources for inclusion in DNP to Treasury. Many of the
data sources included, or designated for inclusion, in DNP were originally
created to comply with legal requirements or manage specific federal
programs. OMB and Treasury have since repurposed them to support
more widespread payment and eligibility determinations.
The nine DNP data sources we selected for analysis, from the broader
set of 28 data sources, and a brief description of each are listed in table
1.
Table 1: Description of the Do Not Pay Working System Data Sources GAO Selected
Owner
Data source Description
General Services Administration
Federal Audit Clearinghouse (FAC) Contains single audit reports and indicates whether a significant deficiency or material
weakness was found and addressed. The Single Audit Act and related guidance require
certain nonfederal entities to undergo a single audit or, in select cases, a program-
specific audit. This audit evaluates an entity’s financial statements and compliance with
federal awards, and entities must submit the results to FAC.
System for Award Management (SAM) Contains publicly available entity registration data for those entities that are registered in
Entity Registrations SAM to conduct business with the federal government in accordance with the Federal
Acquisition Regulation and 2 C.F.R. Part 200 (2026).
SAM Exclusions Identifies parties excluded from receiving federal benefits and awards such as contracts
and federal financial assistance.
Department of Health and Human Services - Office of Inspector General
List of Excluded Individuals and Entities Contains current information about individuals and entities excluded from participation in
Medicare, Medicaid, and all other federal health care programs.
Department of the Treasury - Internal Revenue Service (IRS)
1631 U.S.C. § 3354(a)(2).
Page 8 GAO-26-107466 Federal Data Interoperability
Owner
Data source Description
Automatic Revocation of Exemption List Contains information about nonprofit entities whose tax-exempt status was automatically
revoked because they did not file Form 990 for 3 consecutive years. Tax-exempt
organizations must file an annual information return or notice with the IRS, unless an
exception applies. Annual information returns include Form 990, Form 990-EZ, and Form
990-PF.
Form 990-N list Contains Form 990-N data. Form 990-N is an annual electronic notice that most small
tax-exempt organizations (i.e., organizations with annual gross receipts normally
$50,000 or less) are eligible to file instead of Form 990 or Form 990-EZ.
Publication 78 list Lists organizations that are eligible to receive tax-deductible charitable contributions.
Treasury - Office of Foreign Assets Control (OFAC)
Specially Designated Nationals and Contains a list of individuals, groups, and entities owned or controlled by, or acting for or
Blocked Persons (SDN) List on behalf of, sanctioned countries. It also lists individuals, groups, and entities, such as
terrorists and narcotics traffickers, designated under programs that are not country
specific. Such persons’ assets are blocked, and U.S. persons are generally prohibited
from transacting or dealing with them.
Non-SDN Consolidated Sanctions List Includes parties subject to specific, limited prohibitions, rather than full blocking. It
consolidates individuals, groups, and entities from various OFAC lists, such as the
Sectoral Sanctions Identifications, Foreign Sanctions Evaders, and others with targeted
restrictions.
Source: GAO analysis of agency information. | GAO-26-107466
Note: While OFAC’s Non-SDN Consolidated List has not specifically been designated for inclusion in
DNP, it is included in the SAM Exclusions, which were designated and are part of DNP. Therefore,
we included it in our selection of data sources.
Users of DNP As of July 2025, more than 60 federal government agencies use DNP.
According to Treasury, DNP has demonstrated its ability to prevent
improper payments. For example, Treasury officials told us that in fiscal
year 2025, DNP yielded almost $5 billion in prevention and recovery of
improper payments and fraud (compared to more than $2 billion in fiscal
year 2024). In addition, officials said that almost $7 billion was identified
in fiscal year 2025 using other tools to detect check fraud and conduct
additional screenings (compared to more than $5 billion in fiscal year
2024).
OIGs’ and Our Work on Agency OIGs and we have frequently examined federal award processes;
Award Processes, payment integrity; and agencies’ use of data-matching tools, such as
DNP. We have also reported on opportunities to leverage existing tools,
Payment Integrity, and
such as USAspending.gov, to identify potential fraud, waste, and abuse
Use of Federal Data and to inform enforcement of recipient compliance. These reports have
Page 9 GAO-26-107466 Federal Data Interoperability
highlighted the importance of standardized data and effective internal
controls to minimize fraud, waste, and abuse. 17
For example, we have previously reported on agencies’ implementation of
PIIA, including requirements and challenges with managing and reducing
improper payments. 18 We have also reported on challenges agencies
face regarding data matching due to a lack of government-wide data
standards, adherence to existing standards, or system interoperability. 19
We and the OIGs have also reported on improving the quality of data
available through USAspending.gov. 20
While USAspending.gov can be helpful in identifying potential improper
payments and fraud, its current data quality issues limit its usefulness.
Our work has shown that clarifying data standards, enhancing agency
practices, and reinstating OIG oversight could significantly help improve
the website’s effectiveness and usefulness. 21 Our and the OIGs’ work has
also emphasized that stopping improper payments before they happen
17Our prior related work can be found in the issued reports listed in Related GAO Products
at the conclusion of this report.
18Examples of reports include GAO, Program Integrity: Agencies and Congress Can Take
Actions to Better Manage Improper Payments and Fraud Risks, GAO-25-108172
(Washington, D.C.: Mar. 11, 2025), and Payment Integrity: Significant Improvements Are
Needed to Address Improper Payments and Fraud, GAO-24-107660 (Washington, D.C.:
Sept. 10, 2024).
19GAO, Spectrum IT Modernization: NTIA Should Fully Incorporate Cybersecurity and
Interoperability Practices, GAO-25-107509 (Washington, D.C.: May 22, 2025); Grants
Management: Action Needed to Ensure Consistency and Usefulness of New Data
Standards, GAO-24-106164 (Washington, D.C.: Jan. 25, 2024); Public Health
Emergencies: Data Management Challenges Impact National Response, GAO-22-106175
(Washington, D.C.: Sept. 22, 2022); DATA Act: OMB Needs to Formalize Data
Governance for Reporting Federal Spending, GAO-19-284 (Washington, D.C.: Mar. 22,
2019); and DATA Act: OMB and Treasury Have Issued Additional Guidance and Have
Improved Pilot Design but Implementation Challenges Remain, GAO-17-156 (Washington,
D.C.: Dec. 8, 2016).
20Examples of reports include GAO, Federal Spending Transparency: Opportunities to
Improve USAspending.gov Data, GAO-24-106214 (Washington, D.C.: Nov. 7, 2023);
Federal Spending Transparency: OIGs Identified a Variety of Issues with the Quality of
Agencies’ Data Submissions, GAO-22-105427 (Washington, D.C.: July 12, 2022); and
Federal Spending Transparency: Opportunities Exist to Further Improve the Information
Available on USAspending.gov, GAO-22-104702 (Washington, D.C.: Nov. 8, 2021).
21GAO, Emergency Relief Funds: Significant Improvements Are Needed to Ensure
Transparency and Accountability for COVID-19 and Beyond, GAO-22-105715
(Washington, D.C.: Mar. 17, 2022).
Page 10 GAO-26-107466 Federal Data Interoperability
through preventive controls, such as DNP, is more efficient than
recovering them later (“pay and chase”). 22
Agencies May Use
Many Federal Data
Sources to Verify
Entities’ Eligibility
Throughout the Award
Life Cycle
What federal data sources Agencies may use more than 100 federal data sources, including the 28
may agencies use? data sources in DNP or designated for inclusion in DNP, to help
determine if entities, as well as individuals, qualify to receive federal
awards and payments. Some of these sources include information on
identity verification, financial delinquency, criminal records, and prior audit
findings. 23 Agencies may use a combination of data sources to verify
eligibility of recipients. 24 However, many of the data sources we identified
were created to comply with legal requirements or manage specific
federal programs. These data sources were not originally created to
support eligibility determinations for other agencies.
Examples include the following:
• SSA maintains data on identity, disability, U.S. citizenship, incarceration,
and death to administer its own programs. Other agencies may also use
22GAO-25-108172; GAO-24-107660; and Pandemic Response Accountability Committee,
Fraud Alert Follow-up: Improved Sharing of Death Records and Use of the Do Not Pay
System Would Strengthen Program Integrity and Better Protect the Public (Washington,
D.C.: May 11, 2023),
https://www.pandemicoversight.gov/media/file/do-not-pay-fraud-report-follow-5-11-2023pd
f. “Pay and chase” refers to the labor-intensive and time-consuming practice of trying to
recover overpayments once they have already been made rather than preventing
improper payments in the first place.
23Identity verification or proofing confirms that entities or individuals are who they claim to
be, for example, to prevent fraud. Identity eligibility checks help ensure that the entity or
individual is entitled to receive a federal award or benefit, for example, to prevent improper
payments. Federal award programs should adopt both identity verification and identity
eligibility checks to manage risk.
24As previously discussed, PIIA requires executive agencies to use DNP to verify payment
and award eligibility before disbursing federal funds. 31 U.S.C. § 3354.
Page 11 GAO-26-107466 Federal Data Interoperability
these data to verify identity or eligibility for other federal programs (e.g.,
the Department of Housing and Urban Development uses SSA
information to help verify applicant identity or eligibility for subsidized
housing programs).
• The Department of Veterans Affairs maintains data on veterans’ health,
education, and compensation benefits to manage and deliver
personalized benefits and services to veterans. Other agencies may also
use this information when veteran status affects program eligibility (e.g.,
the Small Business Administration uses veterans’ information to help
verify eligibility for contracting opportunities).
• IRS publishes the tax-exempt organizations lists to make available the
status of such organizations. 25 Other agencies may also use these data
to verify eligibility to receive federal funding (e.g., the National Institutes
of Health use IRS tax-exempt status information to monitor whether tax-
exempt hospitals meet federal requirements).
How may agencies access Agencies may access relevant data through public websites, by direct
federal data sources? access to source systems, and through centralized access. For example:
• Public website. Agencies may obtain sanctions-related data from
OFAC’s website. Agencies may also obtain the list of individuals and
entities that are excluded from participation in federal health care
programs, such as Medicare, from the HHS OIG’s website.
• Direct access. Agencies may verify Social Security numbers directly
from the SSA State Verification and Exchange System. Agencies may
also verify institutions, day care home providers, and individuals that
have been terminated or otherwise disqualified from Child and Adult Care
Food Program participation through the Department of Agriculture’s
National Disqualified List.
• Centralized access. Agencies may use the Federal Data Services Hub,
which the Centers for Medicare & Medicaid Services primarily maintains.
It is a single point of access to a variety of federal data sources, including
those from SSA, the Department of Homeland Security, and IRS, to verify
income, citizenship, and other eligibility factors. Similarly, the DNP
Connect and Web Portal provides access to multiple federal databases,
including those from IRS, OFAC, and GSA.
25These lists include IRS’s Automatic Revocation of Exemption List, Form 990-N list, and
Publication 78 list. 26 U.S.C. §§ 6033(j), 6104(b).
Page 12 GAO-26-107466 Federal Data Interoperability
Depending on the type and nature of the data, statutory authorities, and
privacy and security protections involved, access to certain federal data
sources may be limited or require a memorandum of understanding or
computer matching agreement that dictates how agencies may use,
share, and protect the data. 26 For sensitive or restricted data, such as
Social Security numbers or financial records, agencies must meet
additional legal and technical requirements and may only access the data
through secure federal systems that require proper authentication.
Laws such as the Privacy Act of 1974 and the Computer Matching and
Privacy Protection Act of 1988 govern how agencies may access and use
personal data. 27 These laws require agencies to establish safeguards,
document formal agreements, and ensure that they use data only for
authorized purposes. Congress established these protections to balance
efficiency and the government’s need to verify eligibility by protecting the
public’s trust and reducing misuse of personal information.
How do federal data Agencies may use federal data sources to improve program efficiency
sources help agencies and verify if entities, as well as individuals, meet the eligibility criteria for
federal programs throughout the award life cycle, including pre-award
verify eligibility?
screening, post-award monitoring, and payment validation. These data
sources support more informed decision-making, help reduce the risk of
improper awards and payments, identify potential fraud, and ensure
compliance with legal and policy requirements. 28
For example, agency officials, including auditors, may use these data
sources throughout the award life cycle to
• verify identity or status, such as confirming that an individual is not
deceased or that a business is properly registered in SAM;
265 U.S.C. § 552a and Pub. L. No. 100-503, 102 Stat. 2507.
275 U.S.C. § 552a and Pub. L. No. 100-503, 102 Stat. 2507.
28At a minimum, agencies must review, as appropriate, the databases within DNP to verify
eligibility. This means that agencies may determine which, if any, additional databases
beyond the minimum should be reviewed based on the risk and type of payment. 31
U.S.C. § 3354(a). Office of Management and Budget, Transmittal of Appendix C to OMB
Circular A-123, Requirements for Payment Integrity Improvement, OMB M-21-19
(Washington, D.C.: Mar. 5, 2021).
Page 13 GAO-26-107466 Federal Data Interoperability
• check for suspensions, debarments, or other prohibitions that would
disqualify an applicant from receiving federal funds, such as contracts or
payments from federal programs; 29
• confirm compliance and assess eligibility by identifying disqualifying
conditions or risks based on past performance or financial history by
considering factors such as prior audit findings, failure to meet reporting
requirements, or delinquent debt; and
• identify potential fraud risks, such as duplicate or conflicting information
and other inconsistencies or irregularities, that may indicate potentially
fraudulent activity.
Our prior work has shown that these data sources can help corroborate
self-reported information from applicants and enable more accurate and
efficient eligibility determinations. 30 Having access to timely, standardized
federal information also helps reduce the administrative burden on
agencies and improve coordination, among other things.
Data Interoperability
Weaknesses May
Hinder Agencies’
Ability to Efficiently
Determine Award and
Payment Eligibility
What is data Data interoperability refers to the ability to share and disseminate
interoperability, and why standardized data in a way that is efficient, consistent, and accessible
across different systems and users, for which high-quality data are
does it matter to the
federal government?
29Suspension and debarment are administrative actions to prohibit a recipient from
participating in federal contracts and covered nonprocurement transactions, 2 C.F.R. §§
180.925, 180.1015; FAR 2.1, with the primary difference being duration and legal
standing. According to GSA, suspensions are temporary (usually up to 12 to 18 months)
pending investigation, while debarments are final, longer-term sanctions (typically up to 3
years) imposed after a finding of misconduct.
30Examples of reports include GAO, Fraud and Improper Payments: Data Quality and a
Skilled Workforce Are Essential for Realizing Artificial Intelligence’s Benefits,
GAO-26-108850 (Washington, D.C.: Jan. 13, 2026), and Federal LowIncome Programs:
Use of Data to Verify Eligibility Varies Among Selected Programs and Opportunities Exist
to Promote Additional Use, GAO-21-183 (Washington, D.C.: Feb. 25, 2021).
Page 14 GAO-26-107466 Federal Data Interoperability
essential. 31 OMB guidance directs agencies to promote open,
interoperable data by building or modernizing information systems using
machine-readable formats, data standards, and consistent metadata to
support downstream data processing. Agencies are also required to
ensure that the information is reliable by implementing rigorous pre-
dissemination practices that evaluate accuracy and coherence. 32
This approach to data interoperability is critical to support federal
government efforts emphasizing the use of existing data. It requires high-
quality program data to accurately evaluate the effectiveness of
government programs and policies. To increase the integrity of analyses
based on such data, the National Academies of Sciences, Engineering,
and Medicine recommend a comprehensive quality framework that
includes evaluating and documenting data interoperability concerns,
including timeliness, accuracy, accessibility, coherence, interpretability,
and granularity. 33 Because data interoperability depends on quality and
consistency, maintaining strong data standards is essential. This includes
ensuring that formats are standardized, data validation checks are
effective, and agencies address inaccuracy issues, such as missing or
invalid values, and duplicates.
Data interoperability allows federal data systems to exchange information
and collaborate seamlessly, enabling more timely, informed, and
evidence-based decision-making and increasing the efficiency and
effectiveness of federal operations. In contrast, poor data quality or
inconsistent data structures break down interoperability, preventing
accurate or efficient record matching and data integration, which
undermines the usefulness of the data. Consequently, relying on poorly
matched or poorly integrated data limits agencies’ ability to inform
decision-making and to implement efficient and reliable processes,
31The federal government has generally defined interoperability from a system
perspective, not on data contained in those systems. As such, we developed this definition
for purposes of this report based on OMB guidance discussed and our research of related
laws and our prior work, among other things. E.g., 44 U.S.C. § 3601(6).
32Office of Management and Budget, Phase 2 Implementation of the Foundations for
Evidence-Based Policymaking Act of 2018: Open Government Data Access and
Management Guidance, OMB M-25-05 (Washington, D.C.: Jan. 15, 2025); Improving
Implementation of the Information Quality Act, OMB M-19-15 (Washington, D.C.: Apr. 24,
2019); and Open Data Policy-Managing Information as an Asset, OMB M-13-13
(Washington, D.C.: May 9, 2013). OMB M-25-05 rescinded and replaced OMB M-13-13.
33OMB M-19-15.
Page 15 GAO-26-107466 Federal Data Interoperability
including the potential use of artificial intelligence to assist users in
making eligibility determinations.
Within the context of eligibility determinations, the lack of data
interoperability across systems may limit agencies’ ability to efficiently
and reliably determine whether a recipient is identified as ineligible in the
relevant data sources. For the same reasons, officials might also be
unable to determine whether previous payments were improperly made to
ineligible recipients, thereby increasing the risks of improper awards or
payments going undetected.
What are the requirements For more than 30 years, several laws and OMB and Treasury guidance
for federal data have established general requirements related to data interoperability and
standardization, but they have not established specific requirements for
interoperability?
enforcing data interoperability, such as for recipient eligibility data,
throughout the federal government.
Legal Requirements Related to We identified 12 laws and one executive order that promote federal data
Data Interoperability interoperability. For example, the Digital Accountability and Transparency
Act of 2014 (DATA Act) and the Grant Reporting Efficiency and
Agreements Transparency Act of 2019 (GREAT Act) include
requirements to establish data standards and definitions for spending and
grant data, respectively. In contrast, laws such as the Paperwork
Reduction Act of 1995, E-Government Act of 2002, and the Foundations
for Evidence-Based Policymaking Act of 2018 promote key principles,
such as data standardization, sharing, reuse, and transparency. These
are important aspects of data interoperability, but they do not address the
establishment of government-wide data governance to establish specific
requirements and help enforce interoperable data across the federal
government. We summarize relevant laws and one executive order
related to data interoperability in table 2.
Table 2: Laws and an Executive Order with Requirements Relevant to Data Interoperability for Federal Agencies
Law or executive order Requirements relevant to data interoperability
Federal Managers’ Financial Integrity Act of 1982, Pub. L. Requires executive branch entities to establish systems of internal control
No. 97-255, 96 Stat. 814, codified at 31 U.S.C. § 3512(c), in accordance with Comptroller General standards to ensure accurate
(d) accounting, safeguarding of assets, and reliable financial reporting.
Paperwork Reduction Act of 1995, Pub. L. No. 104-13, Assigns the Office of Management and Budget (OMB) responsibility for
109 Stat. 163, codified as amended at 44 U.S.C. § 3501 federal information dissemination and mandates the development of
et seq. common standards for federal information, including interoperability.
Directs OMB to develop and oversee the implementation of uniform
information resources management policies, principles, standards, and
guidelines.
Page 16 GAO-26-107466 Federal Data Interoperability
Law or executive order Requirements relevant to data interoperability
Information Technology Management Reform Act of 1996 Establishes agency chief information officers and tasks them with
(also known as the Clinger-Cohen Act of 1996), Pub. L. developing and implementing integrated IT architectures.
No. 104-106, div. E, 110 Stat. 186, 679, codified at 40
U.S.C. § 11101 et seq.
Federal Financial Management Improvement Act of 1996, Requires Chief Financial Officers Act of 1990 (CFO Act) agencies—24
Pub. L. No. 104-208, title VIII, §§ 801 et seq., 110 Stat. major executive departments and large independent agencies—to
3001, 300-389-394, codified at 31 U.S.C. § 3512 note implement and maintain financial management systems that comply with
federal requirements, applicable accounting standards, and the U.S.
Standard General Ledger at the transaction level. It promotes consistency
in agency accounting practices across fiscal years and the use of uniform
accounting standards.
Information Quality Act of 2000, Pub. L. No. 106-554, app. Requires OMB to issue guidance to federal agencies for ensuring and
C, title V, § 515, 114 Stat. 2763, 2763A-153-155 maximizing the quality, objectivity, utility, and integrity of disseminated
information.
E-Government Act of 2002, Pub. L. No. 107-347, 116 Establishes the Chief Information Officer Council and the Interagency
Stat. 2899, codified at 44 U.S.C. § 3601 et seq. Committee on Government Information to promote government
information interoperability and IT standards.
Federal Funding Accountability and Transparency Act of Establishes USAspending.gov and requires federal agencies to report
2006 (FFATA), Pub. L. No. 109-282, 120 Stat. 1186, information on federal awards to entities over $25,000, including unique
codified at 31 U.S.C. § 6101 note identifiers for the award recipient and, if applicable, the parent entity (the
organization that owns another entity).
Digital Accountability and Transparency Act of 2014, Pub. Expands FFATA and requires the Department of the Treasury and OMB
L. No. 113-101, 128 Stat. 1146, codified at 31 U.S.C. § to establish government-wide financial data standards for federal funds,
6101 notea including the use of common data elements and unique identifiers, to
submit data on USAspending.gov. It also aimed to hold federal agencies
accountable for the completeness, timeliness, quality, and accuracy of the
data they submitted through reviews by their Offices of Inspector General
and GAO, but that requirement sunset in 2021.
21st Century Integrated Digital Experience Act, Pub. L. Requires agencies to standardize and modernize public-facing websites
No. 115-336, 132 Stat. 5025, codified at 44 U.S.C. § 3501 and digital services by avoiding duplicative legacy sites, ensuring
note searchable content, and maintaining common standards that support
future shared service interoperability.
Foundations for Evidence-Based Policymaking Act, Pub. Establishes a Chief Data Officer Council within OMB required to set
L. No. 115-435, title II, 132 Stat. 5529, 5534-5544, government-wide best practices for the use, protection, dissemination,
codified in scattered sections of 44 U.S.C. ch. 35, subch. I and generation of data. While the council’s statutory authorization expired
(2019) on December 15, 2024, the act continues to mandate that agencies
develop and maintain comprehensive data inventories, among other
requirements.
Grant Reporting Efficiency and Agreements Transparency Requires OMB and a standard-setting agency to establish government-
Act of 2019, Pub. L. No. 116-103, 133 Stat. 3266, codified wide data standards for information that recipients of federal grants and
at 31 U.S.C. § 6401 et seq. other financial assistance report, including standardized definitions for
data elements and unique identifiers.
Payment Integrity Information Act of 2019, Pub. L. No. Encourages executive agencies to enter into computer matching
116-117, 134 Stat. 113, codified at 31 U.S.C. § 3351- agreements with other executive agencies to allow ongoing data matching
3358 (2020) (e.g., automated data matching) to detect and prevent improper
payments.
Page 17 GAO-26-107466 Federal Data Interoperability
Law or executive order Requirements relevant to data interoperability
Protecting America’s Bank Account Against Fraud, Directs Treasury and OMB to strengthen fraud prevention and improper
Waste, and Abuse, Exec. Order No. 14249, 90 Fed. Reg. payment verification by expanding data access, enabling precertification
14,011, (Mar. 28, 2025) checks for all Treasury-disbursed payments, and guiding agencies on
data sharing for payment verification. It also requires CFO Act agencies to
consolidate, standardize, and integrate their core financial systems with
Treasury platforms.
Source: GAO analysis of the laws and executive order. | GAO-26-107466
a
In March 2022, we recommended that Congress consider extending requirements for the Offices of
Inspector General to periodically report on USAspending.gov data, among other things. As of
February 2026, Congress had not enacted legislation to address these matters. See
GAO-22-105715.
Data Interoperability Provisions We identified several guidance documents for agencies that relate to
from Guidance federal data interoperability. For example, Treasury and OMB guidance
for USAspending.gov reporting outline specific standards, definitions, and
requirements for data submitted under the DATA Act. In contrast, other
OMB guidance about federal data broadly requires agencies to leverage
data standards to promote alignment, comparability, and reuse of data
but does not provide specific data standards, definitions, or requirements.
We summarize relevant guidance in table 3.
Table 3: Guidance Relevant to Data Interoperability for Federal Agencies
Guidance Relevant data interoperability guidance
Office of Management and Budget (OMB) M-15-12, Provides guidance to federal agencies on reporting requirements under the
Increasing Transparency of Federal Spending by Federal Financial Accountability and Transparency Act of 2006 (FFATA), as
Making Federal Spending Data Accessible, amended by the Digital Accountability and Transparency Act of 2014 (DATA
Searchable, and Reliable (May 8, 2015) Act). It directs the use of standardized data definitions published in a virtual
repository and requires Treasury to publish spending data on
USAspending.gov.
OMB Circular A-130, Managing Information as a Establishes government-wide policy for managing federal information and IT
Strategic Resource (July 27, 2016) resources, directing agencies to use open data standards, application
programming interfaces (API), and machine-readable formats to support
interoperability. It also requires agencies to implement policies, procedures, and
standards that enable data governance.
OMB M-19-15, Improving Implementation of the Provides agencies with additional guidelines for their responsibilities under the
Information Quality Act (Apr. 24, 2019) Information Quality Act, including requirements for pre-dissemination quality
reviews and updates to agency information quality assurance procedures. It
also reinforces that agencies are required to create information that supports
public transparency and enables third-party use, ensuring that data are
accessible and usable. Additionally, it emphasizes that agencies must provide
potential users with sufficient information about data quality, strengths,
weaknesses, and analytical limitations when disseminating it publicly.
OMB M-19-18, Federal Data Strategy – A Establishes the Federal Data Strategy as a government-wide framework to
Framework for Consistency (June 4, 2019) guide federal agencies in managing and using data as a strategic asset,
requiring agencies to prioritize data governance and leverage data standards to
maximize data quality and facilitate access, sharing, and interoperability.
Page 18 GAO-26-107466 Federal Data Interoperability
Guidance Relevant data interoperability guidance
OMB M-19-23, Phase 1 Implementation of the Provides implementation guidance of the Foundations for Evidence-Based
Foundations for Evidence-Based Policymaking Act Policymaking Act, Pub. L. No. 115-435, title II, 132 Stat. 5529, 5534-5544,
of 2018 (July 10, 2019) codified in scattered sections of 44 U.S.C. ch. 35, subch. I (2019), in support of
statutory establishment of agency data governance roles and responsibilities for
key personnel, including the newly designated Chief Data Officer (CDO)
position. It also directs the CDO Council to develop government-wide best
practices, promote data sharing agreements, and identify ways to improve the
collection, access, and use of data.
OMB M-20-12, Phase 4 Implementation of the Requires agencies to support the secondary use and dissemination of
Foundations for Evidence-Based Policymaking Act information. Notes that one effective approach is to enable reuse of evaluation
of 2018: Program Evaluation Standards and data by providing clear data dictionaries or other documentation describing data
Practices (Mar. 10, 2020) sources, defining data elements, and outlining any limitations in completeness
or accuracy of the data.
OMB M-21-19, Transmittal of Appendix C to OMB Provides a payment integrity framework to guide agencies authorized to enter
Circular A-123, Payment Integrity Improvement into ongoing computer matching agreements to detect and prevent improper
(Mar. 5, 2021) payments.
OMB M-21-27, Evidence-Based Policymaking: Reaffirms and expands on previous OMB guidance on learning agendas and
Learning Agendas and Annual Evaluation Plans annual evaluation plans, including OMB M-19-23 and OMB M-20-12. It provides
(June 30, 2021) additional guidance for implementing the Foundations for Evidence-Based
Policymaking Act to support data-sharing efforts as part of evidence-based
government.
OMB M-23-22, Delivering a Digital-First Public Directs agencies to deliver digital services using structured, machine-readable
Experience (Sept. 22, 2023) content and standardized interfaces, such as web APIs, to enable data
exchange and promote interoperability across different systems.
Treasury Governmentwide Spending Data Model, Provides government-wide data definition standards and technical guidance for
GSDM 1.2 (Dec. 2025) federal agencies on what to report to USAspending.gov. The Department of the
Treasury and OMB first released it as the DATA Act Information Model Schema
in 2016, and Treasury continually updates it to reflect new legislation and
policies. It includes data-reporting submission specifications, such as data
elements, character limits and valid values, and validation rules.
OMB M-25-05, Phase 2 Implementation of the Requires agencies to develop and maintain a comprehensive data inventory of
Foundations for Evidence-Based Policymaking Act their data assets that uses the OMB-approved standard metadata schema, is
of 2018: Open Government Data Access and published in open and machine-readable formats, and is interoperable with the
Management Guidance (Jan. 15, 2025) Federal Data Catalog that the General Services Administration maintains.
OMB M-25-06, Re-establishing the Chief Data Reestablishes the CDO Council to establish government-wide best practices for
Officer Council (Jan. 15, 2025) federal data, promote interagency data-sharing agreements, and identify new
technology solutions for improving the collection and use of data.
Source: GAO analysis of guidance. | GAO-26-107466
Who is responsible for While several agencies and cross-agency groups support coordination of
data interoperability best practices for data management and interoperability, there is no data
governance agency designated to establish and enforce interoperability
across the federal
requirements across the federal government. Based on our research,
government? these agencies and cross-agency groups identify shared needs and
challenges, provide technical and policy guidance, and develop
frameworks to enable more consistent data use and exchange. We found
that none of them issue mandatory directives and some of them have
Page 19 GAO-26-107466 Federal Data Interoperability
limited technical and operational enforcement capacity. As a result,
interoperability efforts—including those related to eligibility data
sources—remain fragmented and depend on agencies’ voluntary
adoption. Table 4 includes the stakeholders and a summary of their roles
and responsibilities that we identified based on our research and
interviews with agency officials regarding data interoperability.
Table 4: Stakeholders of Federal Data Interoperability That GAO Identified and Their Management Areas, Roles, and
Responsibilities
Stakeholder Management area Roles or responsibilities
Office of Management and Budget Government-wide data policy Encourages agencies to adopt data standardization and
(OMB) interoperability practices through the Federal Data Strategy
and implementation guidance for the Foundations for
Evidence-Based Policymaking Act. It also co-leads
implementation of the Digital Accountability and
Transparency Act of 2014 (DATA Act), ensuring transparent,
accessible financial data. Further, it collaborates with the
Federal Committee on Statistical Methodology—an
interagency committee that OMB founded in 1975 comprising
career federal employees that OMB selected based on their
expertise in statistical methods—to promote consistent
statistical standards and methods for federal data collection
and use.
Department of the Treasury Financial and award data Co-leads implementation of the DATA Act, requiring agencies
reporting and disbursements to report financial and award data in standardized formats in
systems such as USAspending.gov and the Financial
Assistance Broker Submission—a system through which
federal agencies report detailed financial assistance award
data—to enforce data uniformity across agencies. It also
manages several government-wide financial data systems,
including the Governmentwide Treasury Account Symbol
Adjusted Trial Balance and other systems that support the
preparation of the consolidated financial statements of the
U.S. government. Furthermore, Treasury operates the Do Not
Pay working system and various federal payment systems
that disburse funds on behalf of agencies.
General Services Administration Data platforms and technology Supports interoperability through modernizing and
(GSA) centralizing data systems through Integrated Award
Environment systems, such as SAM.gov, which is part of the
federal awards life cycle, and Data.gov, which is a central
repository for federal open data.
National Information Exchange Data sharing framework and data Provides a framework developed through interagency and
Model (NIEM) Opena model intergovernmental collaboration to support consistent data
exchange and interoperability. It includes a standardized data
vocabulary model for use in community-specific business
areas, such as emergency management, human services,
and forensic data sharing. Agencies may choose to adopt its
standards but are not required to do so.
Page 20 GAO-26-107466 Federal Data Interoperability
Stakeholder Management area Roles or responsibilities
Chief Data Officer Council Cross-agency data strategy Coordinates data-sharing efforts and promotes best practices
for the management, use, protection, dissemination, and
generation of data between federal agencies. Established
under the Foundations for Evidence-Based Policymaking Act,
the council comprises chief data officers from the 24 major
executive departments and large independent agencies (i.e.,
the 24 Chief Financial Officer Act of 1990 (CFO Act)
agencies), other designated federal agencies, and officials
from OMB. It engages with stakeholders to help improve
access to federal data. It also publishes guides, playbooks,
and reports, such as recommendations related to data
sharing. Further, the council shares responsibilities with other
interagency councils that conduct and affect data-related
activities, including those focused on IT, statistics, information
security, evaluation, and privacy.
Interagency Council on Statistical Statistical data integration Advises OMB and coordinates statistical methods and
Policy systems to support interoperability of survey, census, and
administrative data, and to help ensure secure, shared
access to data throughout the Federal Statistical Research
Data Centers. The council comprises 30 unique members,
including the Chief Statistician, designated statistical officials
from the 24 CFO Act agencies, as well as all heads of OMB
recognized statistical agencies and units.
Shared Services Governance Cross-functional steering Coordinates agency inputs, recommends changes to service
Board committee offerings, and supports government-wide efforts to improve
service delivery efficiency. GSA manages it under OMB
guidance, and it is part of the governance structure for shared
IT and service delivery initiatives, including those that support
data interoperability. The board operates in an advisory and
coordinating capacity. Its membership includes
representation across federal interagency councils, including
the Chief Acquisition Officer Council, Performance
Improvement Council, and Chief Data Officer Council, among
others.
Chief Information Officer Council Cross-agency federal information Advises OMB and the Office of Personnel Management and
management promotes collaboration across the federal government to
improve IT practices related to cybersecurity, cloud adoption,
using data, and IT workforce development. The council also
shares best practices, ideas, experiences, and innovative
approaches to enhance government efficiency and
effectiveness and delivery of services. It comprises OMB
officials and the chief information officers from the 24 CFO
Act agencies, among others.
Page 21 GAO-26-107466 Federal Data Interoperability
Stakeholder Management area Roles or responsibilities
Council on Federal Financial Financial assistance Supports coordination of data standards and practices to help
Assistance improve coordination, transparency, and accountability
related to federal financial assistance awards. This
interagency forum also promotes consistent application and
interpretation of government-wide financial assistance
policies, management practices, and business data
standards, among other requirements. The Council on
Federal Financial Assistance comprises OMB officials, senior
financial assistance officers from the 24 CFO Act agencies,
and a representative from the Small Agency Council.
Source: GAO analysis of policies and guidance. | GAO-26-107466
Note: This list may not be comprehensive. We listed the agencies and groups and a summary of their
roles and responsibilities that we identified based on our research and interviews with regards to
eligibility data sources, including Treasury’s Do Not Pay working system.
a
While the Departments of Justice, Health and Human Services, and Homeland Security originally
established NIEM in 2005, it is not a federal agency. It provides a collaborative partnership among
governmental agencies, operational practitioners, systems developers, standards bodies, and other
stakeholders across federal, state, local, tribal, territorial, international, and private organizations.
What obstacles can affect We identified three obstacles that can affect the interoperability of the
the interoperability of nine selected federal data sources: (1) lack of comprehensive data
dictionaries, (2) lack of comprehensive or documented data validation
selected federal data rules, and (3) inconsistencies between data sources. 34 Without clear
sources? requirements for data standards, agencies have developed datasets that
use inconsistent definitions and validation rules, contributing to data
quality issues. These weaknesses and inconsistencies in data
management reduce the accuracy and completeness of the data and
increase the difficulty of sharing and integrating data across systems.
Incomplete Data Dictionaries Data dictionaries for eight of the nine selected data sources we reviewed
were incomplete. As shown in table 5, these eight selected data sources
did not include all six of the following key elements that we developed
from practices identified in our prior work, as well as data standards,
policies, and processes that federal agencies developed or use. 35
• Name. A word or set of words that the field is called.
• Definition. Description of what data elements represent.
34A data dictionary is an agreed-upon set of clearly and consistently defined elements,
definitions, and attributes.
35Sources for practices we identified included GAO reports GAO-25-107509,
GAO-19-284, and GAO-17-156; National Institute of Standards and Technology guidance;
and policies and processes federal agencies use, such as the Governmentwide Spending
Data Model (GSDM).
Page 22 GAO-26-107466 Federal Data Interoperability
• Character limit. Limit in the number of characters allowed for a field.
• Data type and format. Description of the expected value types, such as
text, string, numbers, or a date in a specific order (e.g., MM/DD/YYYY).
• Mandatory or optional field indicator. Clarification of whether a field is
required or optional, including conditional circumstances.
• Sensitivity indicator. Identification of data elements that contain
sensitive or restricted information.
Table 5: GAO Assessment of Data Element Specifications in Data Dictionaries for the Nine Selected Data Sources
Data type Mandatory or
Owner and optional field Sensitivity
Data source Name Definition Character limit format indicator indicator
General Services Administration
Federal Audit Clearinghouse Yes Yes No Yes Yes NA
System for Award Management Yes Yes Yes Yes Yes Yes
(SAM) Entity Registrations
SAM Exclusions Yes Yes Yes No No NA
Department of Health and Human Services - Office of Inspector General
List of Excluded Individuals and Yes Yes Yes No Yes No
Entities
Department of the Treasury - Internal Revenue Service (IRS)
Automatic Revocation of Yes Yes Yes No No NA
Exemption List (ARL)a
Form 990-N list Yes Yes Yes No Yes NA
Publication 78 lista Yes Yes Yes No No NA
Treasury - Office of Foreign Assets Control (OFAC)
Specially Designated Nationals Yes No No Yes No NA
and Blocked Persons (SDN) Listb
Non-SDN Consolidated Yes No No Yes No NA
Sanctions Listb
Legend: Yes = agency data dictionary documentation included the key element; No = agency data dictionary documentation did not include the key
element; NA = Not applicable because all information provided in the related files is public.
Source: GAO analysis of agency documentation. | GAO-26-107466
a
IRS did not publish and could not provide a data dictionary upon request for the ARL and the
Publication 78 list. In March 2025, after our request, IRS published on its website a data dictionary for
each of these data sources.
b
OFAC has different data dictionaries for different file formats. We evaluated the data dictionary for
the Extensible Markup Language (XML) file only. XML is a flexible, nonproprietary set of standards for
tagging information so that it can be transmitted using internet protocols and other computer systems
can readily interpret it.
We found no specific federal requirements that define what a data
dictionary should include, which has led to variation in how agencies
Page 23 GAO-26-107466 Federal Data Interoperability
manage documentation for their data. As such, the lack of complete and
standardized data dictionaries contributed to other interoperability
challenges. For example, inconsistent data elements and formats across
sources increased the level of effort required to achieve consistency
across the data sources in our analyses. These types of interoperability
challenges that we encountered could also affect agencies and result in
them using inconsistent or incomplete information for decision-making.
For example, program staff processing federal payments or awards could
have unclear or inconsistent data when making eligibility determinations,
which increases the risk of errors and improper payments or awards.
Inconsistent Data Elements
We found that different data sources used different data element field
names, numbers of data elements, and orders of data element fields to
represent the same information. For example, while Treasury and OMB
collaboratively have developed specific standards for some of these
elements for certain platforms, such as USAspending.gov, there is no
government-wide standard for reporting name and address. Table 6
shows examples of the fields used to represent names in the selected
data sources.
Table 6: Examples of Fields Representing Names in the Selected Federal Data
Sources
Data source Fields representing “Name”
System for Award Management Entity Registrations legal business name
dba name
Publication 78 list business name
List of Excluded Individuals and Entities lastname
firstname
midname
busname
Specially Designated Nationals and Blocked Persons FormattedFirstName
List FormattedLastName
FormattedFullName
Legend: dba = doing business as
Source: GAO analysis of agency documentation. | GAO-26-107466
Our prior work has found that, without clear data elements and definitions,
agencies have interpreted and applied the definitions for the same data
elements differently. This resulted in the reporting of data that were not
Page 24 GAO-26-107466 Federal Data Interoperability
comparable as well as difficulties for users in understanding and using
those data. 36
Inconsistent data elements may also lead to additional efforts, such as
extra time and resources needed to combine and compare data across
different data sources, and increase the risk that management may
misinterpret information when making decisions.
Inconsistent Data Formats
We also encountered formatting differences, such as different character
limits, and names appearing in all capital letters in one source and mixed
case in another source. For example, LEIE limited name fields to a fewer
number of characters than did SAM Exclusions, resulting in a middle
name being truncated. This difference caused a record to appear as a
mismatch between the two sources, even though it referred to the same
individual.
We also found that different data sources reported address and country
information in a variety of formats. For example, entries for country
information in the different data sources included United States, USA, and
US. These variations could reflect the use of different standards or
formatting options available (e.g., Geopolitical Entities, Names, and
Codes and International Organization for Standardization). Without clear
documentation that specifies the required standard and formatting,
determining the reason for the mismatches can be time consuming and
require additional efforts to reconcile and integrate data. It may also result
in the inability to compare data elements that represent the same
information across data sources.
36GAO-24-106164.
Page 25 GAO-26-107466 Federal Data Interoperability
Types of unique identification numbers Lack of Common Unique Identifiers
used in the selected federal data sources
Unique entity identifier is an identification There was no common unique identifier across all the selected data
number that SAM.gov assigns to entities
doing business with the federal government.
sources, as shown in table 7. Not all entities included in the data sources
The General Services Administration has we reviewed conduct business with the federal government, such as
required it since April 4, 2022, when it foreign entities, or are required to have a UEI. The EIN was available in
replaced the Dun & Bradstreet number.
Employer identification number is an
certain formats, such as restricted or sensitive files or on the data source
identification number that the Internal owners’ websites when known in advance.
Revenue Service issues to administer federal
tax laws for businesses, tax-exempt
organizations, and other entities.
National Provider Identifier is a unique
identifier that the Centers for Medicare &
Medicaid Services issues to covered health
care providers for use in Health Insurance
Portability and Accountability Act
administrative and financial transactions.
Source: GAO analysis of agency information. |
GAO-26-107466
Table 7: Common Unique Identifiers Captured in the Nine Selected Data Sources
Common unique identifiers
Owner Unique entity Employer identifier National Provider
Data source identifier (UEI) number (EIN) Identifier
General Services Administration
Federal Audit Clearinghouse ✓ ✓
System for Award Management (SAM) Entity Registrations ✓ ✓a ✓
SAM Exclusions ✓ ✓b ✓
Department of Health and Human Services - Office of Inspector General
List of Excluded Individuals and Entities ✓c ✓
Department of the Treasury - Internal Revenue Service
Automatic Revocation of Exemption List ✓
Form 990-N list ✓
Publication 78 list ✓
Treasury - Office of Foreign Assets Control
Specially Designated Nationals and Blocked Persons (SDN) List
Non-SDN Consolidated Sanctions List
Legend: ✓ = Unique identifier is present in the data source for at least some records.
Source: GAO analysis of agency data. | GAO-26-107466
Note: The availability of common unique identifiers may vary within each data source, and the unique
identifier may not be present for all records.
The EIN is only available in the sensitive file.
a
b
The EIN is not available in the public data files but may be searched in SAM.gov if known in
advance.
Page 26 GAO-26-107466 Federal Data Interoperability
c
The EIN for entities (and Social Security number for individuals) is available in the restricted file, not
the public file, and may be used to verify the provider in the online tool after a name match, if known
in advance.
We aimed to work exclusively with publicly accessible data, which reflect
the type of information agency officials would have, at a minimum,
available for their decision-making. Without accessible and reliable
common unique identifiers in our analyses, we sometimes relied on
individual and entity names or other fields, such as country, to match
records between data sources. These fields varied in name, formatting,
and number of fields, further complicating exact matching. We used exact
matching techniques to replicate what a human might do and to avoid
making incorrect matches. We did not use “fuzzy matching”—a collection
of techniques that calculate how similar the values are. Fuzzy matching
without human judgment to confirm a match can reduce the accuracy and
reliability of the analysis if it incorrectly identifies matches or misses true
matches. For instance, it can incorrectly identify matches, such as “Global
Tech Industries Inc” in one data source matching with “Global
Technologies Inc” in another data source. While using common unique
identifiers across databases could improve the accuracy and efficiency of
data matching, we have previously reported that it may also pose
challenges, such as those related to implementation and cost as well as
potential security and privacy risks if identifiers were compromised. 37
Comprehensive and required use of data dictionaries for each selected
data source could have helped to mitigate these challenges by clearly
documenting the structure, definitions, and formatting requirements for
each data element. This information would have supported our efforts to
identify and reconcile inconsistencies across sources, such as the varying
data formats and field names and the lack of unique identifiers, thereby
improving the reliability and efficiency of data comparisons.
Insufficient Validation Rules Of the nine data sources we reviewed, eight had data validation rules—
Contribute to Data Quality quality checks that help ensure that data are valid, reliable, and accurate
Issues enough for their intended use—that were insufficient or not properly
37Examples of reports include GAO, Identity Verification: GSA Should Demonstrate Its
Implementation of Policies for Testing Data Backups on Login.gov, GAO-25-107000
(Washington, D.C.: June 3, 2025); Research Security: Strengthening Interagency
Collaboration Could Help Agencies Safeguard Federal Funding from Foreign Threats,
GAO-24-106227 (Washington, D.C.: Jan. 11, 2024); Data Protection: Federal Agencies
Need to Strengthen Online Identity Verification Processes, GAO-19-288 (Washington,
D.C.: May 17, 2019); and Health Information Technology: Approaches and Challenges to
Electronically Matching Patients’ Records across Providers, GAO-19-197 (Washington,
D.C.: Jan. 15, 2019).
Page 27 GAO-26-107466 Federal Data Interoperability
documented for the time frame we selected. In addition, the remaining
data source lacked formal validation rules. For example, GSA officials
stated that validation checks were built directly into SAM. However, they
could not readily provide documentation to describe which rules were
applied to SAM Entity Registrations and how they worked. The lack of
clearly defined or documented validation rules contributed to the quality
issues we found, such as incomplete, inaccurate, or duplicate data, that
undermine data reliability and interoperability.
OMB guidance requires agencies to establish procedures to ensure that
information is reviewed for quality before it is disseminated, but there are
no specific federal requirements or guidance for what these procedures
should be. 38 Therefore, we identified practices based on our prior work
and policies and processes that federal agencies used. 39
Based on the practices identified, we developed and used these five
factors to assess data quality:
• Missing values. Information in data elements is missing, which could
indicate incomplete records.
• Invalid values. Data do not conform to expected values or fall outside of
valid time periods.
• Invalid data formats. Characters and format type for the data field do
not align with the data source owners’ rules and specifications, when
available.
• Conflicting data relationships. Combinations of values between data
elements are illogical.
• Duplicate values. Data entries are either full duplicates (i.e., where all
the values in a record are repeated) or partial duplicates (i.e., where
specific values within a record appear more than once).
All nine data sources we reviewed had at least one data quality issue
based on the factors we assessed, as we summarize in table 8.
38OMB M-19-15.
39Practices identified from GAO reports GAO-19-284 and GAO-17-156 and Treasury’s
GSDM.
Page 28 GAO-26-107466 Federal Data Interoperability
Table 8: Data Quality Issues GAO Found by Data Source
Owner Missing Invalid Invalid data Conflicting data Duplicate
Data source values values formats relationships data
General Services Administration (GSA)
Federal Audit Clearinghouse Unclear ✓ ✓ ✓ ✓
System for Award Management (SAM) Entity ✓ ✓ X ✓ X
Registrations
SAM Exclusions ✓ ✓ ✓ X ✓
Department of Health and Human Services (HHS) - Office of Inspector General (OIG)
List of Excluded Individuals and Entities ✓ ✓ ✓ X ✓
Department of the Treasury - Internal Revenue Service (IRS)
Automatic Revocation of Exemption List ✓ ✓ X ✓ ✓
Form 990-N list ✓ ✓ X ✓ ✓
Publication 78 list ✓ X X NA X
Treasury - Office of Foreign Assets Control
Specially Designated Nationals and Blocked Persons Unclear ✓ NA NA ✓
(SDN) List
Non-SDN Consolidated Sanctions List Unclear X NA NA ✓
Legend: ✓ = Data source had data quality issue based on GAO assessment; X = Data source did not have data quality issue based on GAO
assessment; NA = Not applicable; Unclear = Because the agency did not provide documentation of which elements were optional or required, we were
unable to determine whether the missing values were an issue.
Source: GAO analysis of agency data. | GAO-26-107466
Note: Our analyses identified issues, some of which may have been the result of evolving system and
legal requirements, according to some data source owners. Related to duplicates, GSA, HHS OIG,
and IRS officials told us that these duplicates may be or were valid. These officials also stated that
they do not have control over errors, such as those that external parties or other officials responsible
for entering the data introduced.
Treasury officials told us that all data sources in DNP undergo a
validation process specific to each source at the file level, such as
checking that the file is not empty and there are no unsupported file
extensions. If these checks fail, Treasury notifies the data provider to
correct the issues. In addition, Treasury officials told us the agency lacks
the authority to require agencies that own the databases to adopt specific
data standards or validation rules.
Without established and documented validation rules and procedures,
data source owners may be unable to identify and resolve these issues in
a timely manner. This can limit the reliability of the data for downstream
use and decision-making and increase the resources needed to integrate
data or address errors after data have been shared. In addition, users
may be unable to understand the design and inherent limitations of a
Page 29 GAO-26-107466 Federal Data Interoperability
database, which may reduce data credibility and users’ confidence in the
data.
Missing Values
We found that six of the nine selected data sources had missing values
for selected data elements, such as address or part of the address. 40 For
example, the SAM Exclusions data were missing address line 1 for
137,773 of 146,877 records we tested (more than 93 percent) and the
active date of the exclusion for 13,676 of 172,091 records (almost 8
percent). 41 Missing address information limits the ability to accurately
identify and verify potential recipients. Similarly, without an active date for
an exclusion, users will be unable to efficiently determine when the
exclusion was in effect. This can make it difficult to assess compliance
with federal contract or program requirements or to identify whether an
entity should have been excluded from receiving federal funds at a given
point in time.
In some cases, data may be missing because they are not known—such
as the address of a criminal enterprise—or because of other factors,
including that the data element is not applicable to the record,
requirements changed over time, legacy system limitations exist,
information is not published to protect personal information, and there are
errors in the data. For example, some SAM Exclusion entries related to
OFAC may have been missing parts of the address information because
OFAC’s processes changed over time, among other reasons. According
to OFAC officials, as of February 2026 they were aware that address
information for their SAM Exclusions entries was incomplete; they will
continue updating their SAM Exclusion entries to the extent possible.
GSA officials also told us that they collect but do not publicly publish
address line 1 information for individuals, which represent 137,533 of the
137,773 records missing address line 1.
In addition, GSA officials told us that, for the data we reviewed, they had
different validation rules for data submitted to SAM Exclusions through
the web-based manual entry system and the bulk uploader. For example,
40For the remaining three selected data sources, agencies did not identify required data
elements. Therefore, we were unable to determine if required data for these data sources
were missing.
41According to the data exclusion interface details GSA provided, address line 1 was
required for firms and individuals, while active date of the exclusion was required for all
records; therefore, the denominators of total records are different.
Page 30 GAO-26-107466 Federal Data Interoperability
the manual entry system requires address information for firms and
individuals, while the bulk uploader, which processes an Extensible
Markup Language (XML) file, did not. GSA officials said that, depending
on how data are collected, there can be differences in how they apply
business validation rules to each method. As a result, data that agencies
submitted to SAM Exclusions have not been subject to consistent
validation rules. GSA officials also told us that, as part of modernizing
SAM Exclusions, since early 2026 the system requires address line 1 for
data submitted through both the manual entry and bulk uploader for firms
and individuals when the country is the United States or Canada. We
discuss additional issues related to different data file formats later in this
report.
Invalid Values
We found instances where seven of the nine selected data sources
included invalid data, such as values not defined in the data dictionary
and dates that fell outside valid time periods. For example, the Automatic
Revocation of Exemption List (ARL) included 43 records, out of 1.1
million, with an exemption type not listed in the agency’s official list of
exemption types. According to IRS officials, this error was due to a data
entry issue, which they corrected in response to our inquiry. In the Form
990-N list, we found 100 records where the tax year of the filed form did
not align with the tax period indicated on the form. For example, one
record showed a 2014 tax form used for tax period January 1, 2015,
through December 31, 2015—when a 2015 or 2016 form should have
been used. IRS officials told us that they implemented a check to validate
this time frame in 2017. However, the most recent record we identified is
from 2022. While these exceptions represent a small number relative to
the overall dataset, their presence may contribute to or exacerbate issues
as additional data are incorporated over time.
In addition, we found that SAM Exclusions included active dates for two
exclusion records that were set unreasonably far in the future—in the
years 2084 and 2099. According to GSA officials, these exceptions were
data entry errors that the agencies that entered the information had not
corrected.
We were unable to verify the validity of the UEI values in two of the data
sources. Specifically, we did not find 41,765 of 43,273 UEIs (almost 97
percent) from SAM Exclusions and 1,410 of 90,723 UEIs from FAC
Page 31 GAO-26-107466 Federal Data Interoperability
(almost 2 percent) in SAM entity information. 42 According to GSA officials,
not all UEIs that SAM Exclusions create will have a corresponding SAM
Entity Registration record because the entity may never have registered
in SAM.gov. 43 In addition, GSA officials told us that they could not provide
the list of all the UEIs that SAM Exclusions generated because it would
require significant resources. 44
GSA officials also stated that the lack of corresponding SAM entity
information for FAC UEIs is primarily related to historical records (about
1,300 records) before FAC was fully implemented in GSA in October
2023 and some are related to records with FAC accepted dates prior to
SAM entity registration dates.
However, without a central index to verify or manage the UEIs, the validity
and uniqueness of the identifiers cannot be assured. These types of
issues can lead to incorrect determinations of compliance or eligibility and
could result in the use of invalid UEIs or the creation of duplicate UEIs.
We discuss additional issues related to invalid UEIs later in this report.
Invalid Data Formats
We also found that three of the nine data sources had instances where
data did not conform to the format specified in the data dictionary or the
structure that the originating agency defined. 45 For example, we reviewed
the UEIs included in the selected data sources and verified whether they
conformed to the technical specifications that GSA established (e.g., a
12-character alphanumeric code). We found eight instances in SAM
Exclusions and 35 instances in FAC where the UEI did not meet the GSA
42SAM Entity Registrations published file extracts contain all active entity registrations and
any registrations that have expired in the prior 6 months. To obtain a population of all
UEIs, we supplemented the SAM Entity Registration records with the data for inactive
registrations, ID assigned, and unvalidated ID, but some records may not have been
included in our population due to differences in the download dates and system download
limitations. We refer to the supplemented records as SAM entity information.
43SAM Exclusions business rules require a UEI for any excluded entity classified as a firm
or special entity designation. When these types of entities are excluded and do not have
UEIs already assigned, the SAM Exclusions system assigns a UEI to them without
registration information.
44GSA proposed that we use the SAM Exclusions website or application programming
interface to obtain all UEIs associated with exclusion records. However, this approach
would not specify which UEIs SAM Exclusions generated.
45We did not check formatting for all data elements or sources because the specifications
were not always available.
Page 32 GAO-26-107466 Federal Data Interoperability
specifications. GSA officials told us that they implemented the validation
check for the UEI data element in SAM Exclusions in July 2023 and that
FAC has had UEI validation checks at multiple points during the
submission process since September 2023. The records for all instances
we identified in SAM Exclusions and FAC were created prior to the
implementation of these validation checks.
While GSA’s validation check should help ensure that these issues do not
occur in the future, older data have not been updated and are not subject
to the new rules. There may also be additional issues with earlier years of
data not included in our analysis. Without valid UEI values, agencies may
be unable to accurately identify, track, and assess recipients and entities
across datasets, limiting oversight and eligibility determinations.
Conflicting Data Relationships
In addition, four of the nine data sources we reviewed had instances
where two or more data element values contradicted each other. These
inconsistencies or errors within a data source can affect data
interoperability by creating confusion or misalignment when sharing data
or integrating it with other data sources or systems. For example, in FAC,
we found 32,721 records out of 90,723 (about 36 percent) where the FAC
system acceptance date was before—rather than after—the date the
auditee certified its audit report submission. 46 According to GSA officials,
this may occur because GSA uses the Coordinated Universal Time (UTC)
and subtracts 11 hours from the FAC system acceptance date to
accommodate for the different time zones. For instance, a record in FAC
may display an acceptance date of January 11, 2024, and a certification
date of January 12, 2024. Based on GSA’s explanation, if the acceptance
occurred on January 12, 2024, before 11:00 a.m. UTC, the system’s
subtraction of 11 hours would cause the acceptance date to be recorded
as January 11, 2024. As a result, FAC may display the acceptance date
as occurring prior to the certification date, even though the acceptance
occurred on January 12, 2024. Out of the 32,721 records we found,
31,838 (about 97 percent), had an acceptance date 1 day before the
auditee certified date.
In FAC, we also found about 270 records out of 90,723 (less than 1
percent) where the fiscal year duration was not the expected either 12- or
24-month length of the audit period, which may reflect data errors or
46According to GSA officials, the system acceptance date shows when FAC
acknowledges an audit report submission.
Page 33 GAO-26-107466 Federal Data Interoperability
inconsistencies. An audit performed in accordance with the Uniform
Guidance must cover the auditee’s financial transactions for its fiscal year
(or a 2-year period). 47 GSA officials told us that, while the audit period is
typically 12 months, the audit period covered could be less or more than
12 months. However, based on our analysis of the audit period using the
fiscal year start and end dates, we found that they ranged from 0 days to
7,669 days (about 21 years). 48 GSA officials told us that these were user-
entered fields and noted that audit years are not strictly defined and may
vary in length. GSA also said that more guidance from inspectors general
and auditors is needed to determine allowable date ranges and whether
GSA should set limits.
In the Form 990-N list, we found 16 records out of 1.4 million where the
tax period end date was before the tax period start date. IRS officials told
us that they implemented a validation check in 2017 to confirm that the
dates are chronological, and that they applied the check retrospectively
for tax years 2015 and 2016. However, 10 of the 16 records we identified
were related to tax years 2015 and 2016. We also found 20 records
where the tax year lasted more than 1 year, including one that spanned
from 1969 to 2015. IRS officials told us that some organizations can have
fiscal years as long as 53 weeks, and that they implemented a check to
validate this time frame in January 2017. However, 14 of the 20 records
we identified were outside the 53-week limit, and one was dated
December 2017, which is after the validation check was implemented.
Because validation checks generally apply to new records or are not
retrospectively applied to all existing records, inaccurate or illogical data
entered before IRS implemented the checks will remain in the system.
Our prior work has found that changes in data submission and reporting
guidance and requirements over time may affect the completeness,
consistency, and quality of data. 49 This limits data reliability,
47Although the Single Audit Act requires most single audits submitted to FAC to be
performed annually, certain states, local governments, Indian Tribes, and nonprofit
organizations may instead satisfy their single audit requirements biennially. Audits
conducted biennially cover both fiscal years within the biennial period. 31 U.S.C. §
7502(b); 2 C.F.R. § 200.504.
48Although this was outside the scope of our review, we verified the 7,669-day report and
determined it was likely due to a data entry error because the report included different
dates. However, the dates for the next record in the range, which was for 2,190 days
(about 6 years), were consistent with the report. We did not review other reports because
it would be resource intensive and such verification was outside the scope of our analysis.
49GAO-22-104702.
Page 34 GAO-26-107466 Federal Data Interoperability
comparability, and interoperability, especially when not disclosed. As
such, systems may have difficulty interpreting and properly using the data
and could create mismatches, which could affect eligibility determinations
that rely on accurate information.
Duplicate Data
Seven of the nine selected data sources contained duplicate records, that
is, those that were the same or had some of the same information, such
as unique identifier, name, and address. In some cases, agency officials
explained the basis for the duplication; however, in other cases the
reason was not apparent.
For example, both SAM Exclusions and LEIE contained repeated records
within their respective datasets. GSA officials told us that some duplicates
may be valid, but GSA is not able to validate this as the SAM Exclusions
contain records received from multiple excluding agencies. They also told
us that the system allows users to create duplicate records (e.g., to allow
different agencies to enter the same entity excluded for different reasons),
and it is up to users to determine when to create a duplicate record. HHS
OIG officials told us that it is possible to have multiple LEIE records
because an individual or an entity can be excluded under the same
authority and on the same date in different criminal cases or by two
different courts. They also stated that the instances we identified
represented separate criminal cases that were sentenced on the same
date and listed separately, which they said the law requires.
We also found 369,415 out of 1.4 million records in the Form 990-N list
with duplicate entity names but different EINs. According to IRS officials,
all organizations under a group ruling will have the same entity name,
each with its own EIN. 50
Similarly, we found that FAC had more than 2,331 duplicate records (i.e.,
single audit reports submitted more than once) out of 90,723 based on an
analysis of the UEI and audit year. GSA officials told us that auditees may
have multiple submissions for an audit year, if they need to resubmit their
single audit reports. Officials also told us that, in May 2026, they plan to
50A group ruling refers to the determination that allows multiple organizations with similar
characteristics to be covered under a single tax-exempt status ruling. This is typically used
for charitable, religious, and educational organizations, but it can apply to other types of
tax-exempt entities as well.
Page 35 GAO-26-107466 Federal Data Interoperability
release functionality that allows users to update an existing audit rather
than submit a new one to prevent duplicate audit submissions.
While some duplicate records within each data source may be justified,
unexplained complete or partial duplicates may appear to be errors to
users. While some of these errors were limited in number, they raise
concerns about the overall reliability and usefulness of the data and
emphasize the need for stronger validation controls to ensure data
accuracy and consistency.
The lack of sufficient validation rules and checks at the time of data entry
or implementing validation rules after data has been collected and shared
increases the risk of errors and reduces data quality, efficiency, and
performance across agencies’ processes for making eligibility
determinations. These weaknesses can reduce data and systems
interoperability and confidence in the data, which can lead to inaccurate
and inconsistent eligibility determinations and increase the risk of
potentially improper awards or payments. They may also limit the
potential use of artificial intelligence and advanced analytical tools that
depend on high-quality, consistent, valid data to produce accurate and
useful results. 51
Inconsistencies Between Data Seven of the nine data sources we reviewed had inconsistencies between
Sources them that limited their interoperability and hindered the accuracy and
completeness of the information. 52
51GAO, Artificial Intelligence: An Accountability Framework for Federal Agencies and
Other Entities, GAO-21-519SP (Washington, D.C.: June 2021).
52The seven data sources were GSA’s SAM Exclusions, HHS OIG’s LEIE, IRS’s ARL, the
Form 990-N list, the Publication 78 list, and OFAC’s SDN and Non-SDN Lists.
Page 36 GAO-26-107466 Federal Data Interoperability
Relationship among IRS Automatic Unexpected Overlap
Revocation of Exemption List, Publication
78 list, and Form 990-N list We found records that appeared in both ARL (for organizations failing to
Automatic Revocation of Exemption List file returns required to maintain tax-exempt status) and in the Publication
(ARL) shows nonprofits that lost tax-exempt
status for not filing required returns for 3 78 list (organizations that are currently eligible for tax-exempt status).
consecutive years. These lists are mutually exclusive, so the overlap raises questions about
Publication 78 list shows organizations that the accuracy of the data. Specifically, we found 17,175 of 974,927
currently have valid tax-exempt status.
records (almost 2 percent) that appeared in both ARL and the Publication
Form 990-N bulk download data show the
most recent filing that most small nonprofits 78 list based on EIN.
are eligible to submit. Failing to file the
required form for 3 consecutive years causes
automatic revocation and placement on ARL Furthermore, 5,287 of the 17,175 records (almost 31 percent) did not
and removal from the Publication 78 list. have consistent names between the two data sources, which raises
Source: GAO analysis of Internal Revenue Service (IRS)
information. | GAO-26-107466
questions about the uniqueness of EINs and may lead to confusion when
making decisions based on these data. According to IRS officials,
organizations’ names may differ because organizations may change
names and ARL uses information from the time of revocation, while the
Publication 78 list uses the most current information.
Organizations whose tax-exempt status has been revoked (i.e., they are
in ARL) may not file a Form 990-N unless IRS first reinstates them.
However, we found 23,086 records (about 2 percent) in ARL that did not
appear to have been reinstated but were also listed in the Form 990-N
list. This indicates that entities made Form 990-N submissions while they
were ineligible for the simplified filing process. According to IRS officials,
this may occur if IRS has reinstated the organization but has not updated
ARL.
We also found 14,640 of 974,927 records (almost 2 percent) that were
listed as automatically revoked (i.e., in ARL) before the due date of the
third consecutive return they missed. IRS officials stated that this could
happen for a variety of reasons, such as (1) the timing difference between
when the return was filed and when the system automatically revoked the
tax-exempt status, (2) the organization having been revoked more than
once and the second revocation not being recorded, or (3) the
organization changing its fiscal year (i.e., changed the 12-month period to
track and report its finances) since the last revocation was recorded.
Mismatched Records
According to HHS OIG, SAM Exclusions should have all individuals and
entities listed in LEIE. However, we did not find 13,760 of the 81,867
original LEIE records (about 17 percent) in SAM Exclusions based on
either entity name or National Provider Identifier (NPI). We also did not
Page 37 GAO-26-107466 Federal Data Interoperability
find 3,913 of 71,407 LEIE entries from SAM Exclusions (about 5 percent)
in the original LEIE data based on either the entity name or NPI.
HHS OIG officials explained that the mismatch for records prior to 2007
(about 17 percent of the 13,760 and about 66 percent of the 3,913) is
likely related to manual entry errors, such as extra characters or typos.
For the mismatched records after 2007 (about 83 percent of 13,760 and
34 percent of 3,913), HHS OIG officials told us that records since 2007
have been added to SAM Exclusions through an automated process. The
officials said that some mismatches occurred because LEIE and SAM
Exclusions capture middle names and suffixes differently. However, HHS
OIG officials also said they identified transmission issues beginning in
2018 that likely prevented more than 10,000 of the 13,760 LEIE records
from transferring to GSA’s SAM Exclusions. From 2018 through 2022,
data transfers degraded and prevented some LEIE records from
uploading to SAM Exclusions, and from April 2022 to the present, no
transferred LEIE records have been uploaded to SAM Exclusions.
Although we analyzed fiscal years 2023 and 2024 data, we confirmed
that, as of April 2026, SAM Exclusions had six LEIE records created in
September 2025 and no others since May 9, 2022. HHS OIG officials also
said they are working with GSA to resolve these issues and ensure that
SAM Exclusions reflect data for all exclusions. Related to the NPI, HHS
OIG officials noted that the NPI data element is only updated when a user
or provider notifies them. HHS OIG began collecting NPIs in 2008 and
publishing them in LEIE in 2010.
In addition, according to OFAC, SAM Exclusions should also have all
individuals and entities listed in OFAC’s SDN and Non-SDN Lists. 53
However, we did not find a match in SAM Exclusions for 18,634 of the
41,437 OFAC SDN entries and 1,046 of the 1,579 Non-SDN entries
based on the name and related country information. 54 We also reviewed
the OFAC records within SAM Exclusions and did not find 10,267 of the
36,115 OFAC entries within SAM Exclusions in the original OFAC data
based on the name and related country information. 55 According to OFAC
53The FAR requires contracting officers to verify whether potential contractors are listed as
excluded on various lists, including the OFAC SDN and Non-SDN Lists.
54In OFAC data, an entity can have multiple names and countries. OFAC officials told us
that SAM Exclusions uses the first available address in the OFAC data as the related
address. Accordingly, for this comparison, we used all names and the country associated
with the first listed address in the OFAC data as the related country.
55SAM Exclusions does not distinguish between OFAC SDN and Non-SDN records.
Page 38 GAO-26-107466 Federal Data Interoperability
officials, many of these records did not match for a variety of reasons,
such as (1) OFAC entries with multiple alternate names do not have
stand-alone entries in SAM Exclusions for names considered too
common or generic to support meaningful identification, (2) some OFAC
entries in SAM Exclusions did not have country information or had
country information incorrectly entered as the United States, or (3)
historical records created in SAM Exclusions’ predecessor system did not
include country information for entries related to alternate names because
the legacy system did not record those data.
In May 2026, OFAC officials also told us that they are reviewing all data in
SAM Exclusions and coordinating with GSA to update entries as needed,
including some instances based on our findings. They also noted that
they might not always be able to update some information such as when
addresses are not known, historical, or associated with unrelated
individuals, such as residents of large apartment buildings, which is a
challenge for submitting data that they plan to discuss with GSA.
These issues result in conflicting or incomplete data, increasing the risk
that agencies may not identify ineligible parties during contract, grant, or
payment eligibility verification. Improving data interoperability by ensuring
that all eligibility records align and are consistent would enhance
agencies’ ability to accurately identify ineligible parties and strengthen
program integrity.
What additional data In addition to data flaws, we identified challenges related to (1)
interoperability challenges differences across data sources in how frequently data are reported or
updated, (2) the availability of historical information, and (3) the use of
did we find?
different data file formats. While these challenges are common when
working with multiple data sources, they affect data interoperability.
These challenges may also hinder agencies’ ability to use the data to
make eligibility determinations, especially if agency staff do not have
technical or programming expertise.
Different Reporting Cadences Agencies update the data sources at different frequencies, and the
availability of historical data varies, as we summarize in table 9. These
challenges make it difficult to obtain historical information for the same
period across all the selected data sources. Unaligned data may lead to
inconsistent analysis over the selected period and may result in
misidentifying eligible recipients. The lack of historical data may also
make it difficult for auditors and others to conduct analyses and oversight.
Page 39 GAO-26-107466 Federal Data Interoperability
Table 9: Frequency of Updates and Availability of Historical Information of the Selected Data Sources
Owner
Data source Frequency of updates Availability of historical information
General Services Administration (GSA)
Federal Audit Clearinghousea Basic Search: Real-time From 2016 to present
Advanced Search: Daily
System for Award Management (SAM) Entity Public extract files: Monthly Public: Monthly files available for at least the 2
Registrationsa Sensitive extract files: Daily and most recent months; cumulative semiannual
monthly extracts for May and November, available from
2014 to present
Sensitive: Daily and monthly extracts available
from 2022 to present
SAM Exclusionsa Public extract files: Daily Cumulative semiannual extracts:
- April and October from 2015 to 2021
- April and November from 2022 to present
Department of Health and Human Services - Office of Inspector General
List of Excluded Individuals and Entities (LEIE) Monthlyb Monthly change files available for the previous
12 months
Department of the Treasury - Internal Revenue Service
Automatic Revocation of Exemption List (ARL) Monthlyb Cumulative list, includes historical records
Form 990-N list Weekly Not available on the bulk download data file, but
available on the Tax Exempt Organization
Search tool
Publication 78 list Monthlyb No historical data available
Treasury - Office of Foreign Assets Control
Specially Designated Nationals and Blocked Frequently, as needed Annual extract of changes available by year
Persons (SDN) Lista
Non-SDN Consolidated Sanctions Lista Frequently, as needed Annual extract of changes to the various Non-
SDN Sanctions Lists available by year
Source: GAO analysis of agencies’ information. | GAO-26-107466
Note: In 2025, GSA began deleting SAM Entity Registration records older than 10 years, in
accordance with its record retention policy. Unique entity identifiers without recent active registrations
will remain but with limited data.
a
Data can also be obtained using the application programming interface, which may have a different
update frequency and historical availability.
b
While the updates are monthly, ARL and the Publication 78 list are generally updated on the second
Tuesday of each month and LEIE by the middle of each month.
To identify all individuals and entities that each list included at any point
during fiscal years 2023 and 2024, we could not rely on a single annual
file or download for some of the data sources. 56 This is because these
56We provide additional details about the files we downloaded in app. I.
Page 40 GAO-26-107466 Federal Data Interoperability
lists generally reflect status as of the publication date and do not provide
a complete historical account of changes, such as deletions.
To illustrate, LEIE did not have a single file publicly available for fiscal
years 2023 and 2024. 57 As such, we used the list that HHS OIG published
for exclusions active as of October 2024 and joined it with 26 monthly
supplement files documenting reinstatements and exclusions, to capture
all changes for fiscal years 2023 and 2024. However, combining and
merging files may lead to errors and inefficiencies in reconstructing
historical data.
In addition, for SAM Exclusions, we used the semiannual cumulative
extracts, which could have inadvertently missed any additions,
modifications, or removals that occurred within the same 6-month period.
As a result, the dates included in some of the data sources may not have
aligned with fiscal years 2023 and 2024. 58
Different Data File Formats The selected data sources were in different data file formats. While
agencies should make data available in multiple, nonproprietary formats
to meet the needs of a wide range of users, the lack of consistency can
make it difficult to compare or combine them accurately and requires
more time to clean and reformat the data. For example, as we show in
figure 1, Comma-Separated Values and Pipe Delimited Text files use
different characters to separate data, fixed-field formats rely on precise
character spacing to identify each data element, and XML files use tags
to structure data. We provide additional details of the types of files
publicly available for each data source in appendix II.
Having data in multiple file formats can introduce efficiency and
integration challenges, even if files are machine readable as required. For
example, a data analyst would need extra time and must use a manual
process or write a computer program to convert data into consistent
structures for integration and analysis. This process increases the risk of
errors, such as truncation or dropped values, and could result in failure to
integrate. While artificial intelligence could help automate some of the
processes needed to clean or transform the data, the need for manual
57Obtaining a single file for the time period under our review would have required us to
enter into a memorandum of understanding with HHS OIG. We aimed to work exclusively
with publicly accessible data, which we determined would be the minimum information
available to agency officials for decision-making.
58We provide additional details about the dates included in the data sources in app. I.
Page 41 GAO-26-107466 Federal Data Interoperability
intervention or computer programming to address data inconsistencies
would not be eliminated.
Figure 1: Examples of the Different File Formats of the Selected Data Sources
Note: The data elements in this figure are for illustrative purposes only and may not reflect the actual
data elements included in each data source or file format. We fabricated all entity information in this
figure.
Page 42 GAO-26-107466 Federal Data Interoperability
What can be done to help Congress could take actions to help ensure the development and
improve data enforcement of requirements to improve government-wide data
interoperability for recipient eligibility data. Agencies are required to
interoperability across
manage information as an asset, ensuring that systems are designed to
eligibility data sources? maximize interoperability and accessibility. 59 However, no current
government-wide standards exist to help ensure that the recipient
eligibility data can be used across federal agencies. Specifically, while
various efforts have promoted data interoperability across the federal
government, none have established comprehensive, government-wide
requirements—such as standardized data file formats, definitions, or
oversight bodies—to enforce data standards for recipient eligibility data.
Laws such as the DATA Act and the GREAT Act have required more
uniform data practices for USAspending.gov and financial assistance
reporting, respectively. However, we have previously identified
implementation challenges, such as incomplete data and inconsistencies
in data definitions. 60 In addition, OMB guidance, such as OMB M-19-15
and the Federal Data Strategy, underscores the requirement for agencies
to produce high-quality, transparent, and usable data through the value of
leveraging data standards. However, the guidance lacks detailed
specifications (e.g., data standards, dictionaries, and validation rules) and
enforcement mechanisms.
The federal government does have entities in place that could be
coordinated and used to establish requirements for recipient eligibility
data, including data that support eligibility determinations throughout the
award life cycle. Treasury could establish technical standards because it
supports agencies’ efforts to prevent and detect improper payments. The
department also operates systems that collect, validate, and use
government-wide financial, award, spending, and payment data. The
Chief Data Officer (CDO) Council could help facilitate agency input and
ensure that the standards align with broader federal data governance and
agency practices. In addition, OMB could support consistent
implementation through its policy and budget oversight roles, while
coordinating with relevant federal agencies to ensure alignment across
stakeholders and effectiveness in implementation.
Congress could require these entities to work together in establishing and
implementing data interoperability requirements for recipient eligibility
data sources. Specifically, Congress could assign Treasury explicit
59OMB M-25-05 and OMB M-13-13.
60GAO-24-106164 and GAO-19-284.
Page 43 GAO-26-107466 Federal Data Interoperability
authority to lead, in coordination with the CDO Council and in consultation
with OMB—and others, as needed—the development and implementation
of government-wide data standards and interoperability requirements.
This would include developing and implementing common data elements,
definitions, dictionaries, validation, and disclosures for recipient eligibility
data sources, including relevant financial, award, spending, and payment
data needed to support eligibility determinations throughout the award life
cycle.
This approach would improve coordination, optimize data sharing,
enhance data quality, and support more efficient eligibility determinations
across the federal government. It could also lead to more reliable
reporting, strengthen the foundation needed for the effective use of
artificial intelligence, and enable agencies to use other advanced
analytical tools that depend on accurate and reliable data.
Comparability of
USAspending.gov
and SAM Data and
Efficient Identification
of Potentially
Ineligible Recipients
Could we link SAM data Yes, partially. We were able to link most of the fiscal years 2023 and
with USAspending.gov 2024 USAspending.gov award data to SAM entity information using UEIs.
However, most of the SAM Exclusion records did not have a UEI and
award data to identify
could not be linked to SAM entity information. As such, we could not fully
potentially ineligible analyze the data to identify awards to potentially ineligible recipients using
recipients? UEIs, as we summarize in table 10.
Table 10: Summary of USAspending.gov Awards, System for Award Management (SAM) Exclusions, and SAM Entity
Information Matched Using UEIs, Fiscal Years 2023–2024
Count or
Source amounta Notes
USAspending.gov awards 20.3 million Total contracts and financial assistance awards
Awards with valid UEI 12.8 million Awards with UEI matched to SAM entity information
Awards without UEI 7.4 million Awards with no UEI present, mostly for financial
assistance aggregate awards that do not require a UEI
Page 44 GAO-26-107466 Federal Data Interoperability
Count or
Source amounta Notes
Awards with invalid UEI 2,969 Awards with UEI that did not match SAM entity
information
SAM Exclusion records 172,091
Exclusions with valid UEI 41,822 Exclusions with UEI matched SAM entity information or
SAM Exclusions web search
Exclusions without UEI 128,818 Exclusions with no UEI present, mostly for records that
do not require a UEI (e.g., individuals)
Exclusions with invalid UEI 1,451 Exclusions with UEI that did not match SAM entity
information or SAM Exclusions web search
Matched awards based on the UEI 2,074 With active exclusion at the time of transaction
Total obligations amount of matched awards $626.2 million Obligations as of February 2025
Legend: UEI = unique entity identifier.
Source: GAO analysis of USAspending.gov and SAM.gov data. | GAO-26-107466
Notes: We downloaded USAspending.gov award data for fiscal years 2023 and 2024 on February 3,
2025. While USAspending.gov is the official public source of federal spending information, we have
previously reported issues with the quality of award data that could limit the reliability of matches in
our analysis. See GAO-22-104702 and GAO-24-106214.
We matched based only on the UEIs; records and awards without UEIs were not eligible for
matching. These matches by themselves do not indicate that the awards were improper or involved
fraud, waste, or abuse. We did not evaluate recipient eligibility of the awards to entities included in
SAM Exclusions because our evaluation focused on the feasibility of linking award and exclusion
records; eligibility and compliance determinations are the responsibility of the awarding agencies and
require the specific evaluation of each case. Because we made a nongeneralizable selection of data
sources, our findings may not be used to make inferences about other data sources nor the full
population of awards. For details on our methodology, see app. I.
a
Counts represent number of records, unless otherwise noted. Dollar value is presented in millions.
We identified 2,074 awards with transactions involving 137 entities (based
on the UEI) that were listed in the SAM Exclusion records at the time of
the transaction. 61 As of February 2025, total obligations for these 2,074
awards were $626.2 million. However, these matches by themselves do
not indicate that the awards were improper or involved fraud, waste, or
abuse. Making this determination would require specific evaluation of
eligibility of the entities for each of these awards to assess whether any
awards were improper or involved fraud, waste, or abuse.
The matches we identified represent the minimum number of known
cases. Additional matches may exist, but we could not identify them
because most records in SAM Exclusions and most financial assistance
61Each award generally has multiple transactions throughout its life cycle. We analyzed
award transactions for fiscal years 2023 and 2024 and determined if a UEI was excluded
at the same time as the transaction and summarized the information by award for
reporting purposes.
Page 45 GAO-26-107466 Federal Data Interoperability
awards did not have the UEIs needed for our analysis because they were
not required to have them (e.g., SAM Exclusions do not require UEIs for
individuals, and financial assistance awards that are aggregated to
protect personal identifiable information of individuals do not include UEIs
in USAspending.gov). As a result, the matches we found likely represent
only a portion of the UEIs listed in the SAM Exclusions records that
received awards in fiscal years 2023 and 2024.
The inability to fully analyze SAM Exclusions and USAspending.gov data
is an example of government-wide issues with data matching for recipient
eligibility determinations. Although data matching based on unique
identifiers can support eligibility determinations, such identifiers may have
limitations. For example, they might not always be required or available.
Improved data interoperability—including standardized data elements and
increased interoperability of data elements, such as names and
addresses, across data sources and agencies—could enable more
comprehensive and efficient data matching and improve the
government’s ability to identify potentially ineligible recipients. In contrast,
inconsistent or noninteroperable data may limit data matching, reducing
the ability to identify potentially ineligible recipients and increasing the risk
of improper awards or payments.
Federal agencies may use a wide range of data sources and systems to
Conclusions make eligibility decisions and safeguard against improper payments.
However, we found that data in the selected data sources were not
interoperable, partly because data dictionaries are incomplete, validation
rules are insufficient or not properly documented, and data quality issues
exist. These challenges may also hinder use of artificial intelligence to
assist in making eligibility determinations, a process that relies on timely,
high-quality, and integrated data.
Currently, there is no data governance agency designated to establish
and enforce mandatory data interoperability requirements needed to
support recipient eligibility determinations throughout the federal
government. Until Congress establishes this requirement, oversight and
payment integrity efforts and the government’s ability to effectively
leverage artificial intelligence and advanced analytics to determine
recipient eligibility can be limited. Treasury could be assigned this explicit
authority based on the department’s role in operating systems that collect,
validate, and use government-wide financial, award, spending, and
payment data. With such authority, Treasury could work with the CDO
Council and OMB to establish and implement interagency data
Page 46 GAO-26-107466 Federal Data Interoperability
requirements to help improve agencies’ ability to identify and prevent
improper awards and payments and potential fraud, waste, and abuse.
Congress should consider assigning Treasury explicit authority to lead, in
Matter for coordination with the CDO Council and in consultation with OMB—and
Congressional others, as needed—the development and implementation of government-
wide data standards and interoperability requirements, including data
Consideration definitions, dictionaries, validation rules, and disclosures, for recipient
eligibility data sources, including relevant financial, award, spending, and
payment data needed to support eligibility determinations throughout the
award life cycle. (Matter for Consideration 1)
We provided a draft of this report to Treasury’s Bureau of the Fiscal
Agency Comments Service, OFAC, HHS OIG, IRS, GSA, and OMB for review and comment.
Fiscal Service provided written comments that are reprinted in appendix
III and summarized below. Fiscal Service, OFAC, and HHS OIG provided
technical comments, which we incorporated as appropriate. IRS and GSA
did not have any comments on the report. OMB did not provide
comments on the report.
In its comments, Fiscal Service agreed with the findings of our report. The
bureau stated that its authorities do not extend to requiring other agencies
to adopt specific data standards, validation rules, or interoperability
practices for their source systems that produce eligibility data. Fiscal
Service further said that to be successful in setting government-wide data
standards, Treasury would require clear authority and sufficient
resources, including the authority to lead standardization related to
eligibility data that are designated for use in DNP.
We are sending copies of this report to the appropriate congressional
committees, the Director of the Office of Management and Budget, the
Secretary of the Treasury, the Administrator of the General Services
Administration, and the Inspector General of the Department of Health
and Human Services. In addition, the report is available at no charge on
the GAO website at https://www.gao.gov.
Page 47 GAO-26-107466 Federal Data Interoperability
If you or your staff have any questions about this report, please contact
me at rasconap@gao.gov. Contact points for our Offices of
Congressional Relations and Media Relations may be found on the last
page of this report. GAO staff who made key contributions to this report
are listed in appendix IV.
Paula M. Rascona
Director
Financial Management and Assurance
Page 48 GAO-26-107466 Federal Data Interoperability
List of Requesters
The Honorable James Lankford
Chairman
Subcommittee on Border Management, Federal Workforce and
Regulatory Affairs
Committee on Homeland Security and Governmental Affairs
United States Senate
The Honorable Joni K. Ernst
United States Senate
The Honorable Margaret Wood Hassan
United States Senate
Page 49 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Appendix I: Objectives, Scope, and
Methodology
Methodology
This report describes (1) the federal data sources that agencies may use
to verify entities’ eligibility to receive federal awards, (2) the extent to
which data in the selected federal eligibility data sources are
interoperable, and (3) the extent to which data from selected federal
eligibility data sources can be matched with post-award data from
USAspending.gov to assist agencies’ analysis of awards to potentially
ineligible recipients.
To address objective one, we reviewed laws, regulations, Office of
Management and Budget (OMB) guidance, and federal agencies’
websites and documentation to identify more than 100 federal data
sources that agencies can use to determine recipients’ eligibility for
federal awards. We also obtained an understanding of the Department of
the Treasury Do Not Pay working system (DNP), including its
components, integrated data sources, the purpose of data sources, the
process for designating data sources for inclusion in DNP, and federal
agency participation. In addition, we interviewed OMB staff to understand
their role in the Do Not Pay initiative, and what requirements, if any, exist
to help ensure the interoperability of its data sources. We also interviewed
General Services Administration (GSA) officials; Department of Health
and Human Services’ (HHS) Office of Inspector General (OIG) officials;
and Treasury officials from the Bureau of the Fiscal Service, the Internal
Revenue Service (IRS), and the Office of Foreign Assets Control (OFAC)
to identify and better understand
• federal data sources these agencies own and manage,
• the data sources’ purpose and availability, and
• how the data sources can support agencies’ determinations of recipient
eligibility.
We selected these agencies based on the criteria described in objective
two.
To address objective two, we reviewed laws, regulations, policies, and
OMB guidance related to data interoperability. We judgmentally selected
nine data sources from the 28 DNP data sources we identified in
objective one, to assess the extent to which they were interoperable. We
selected federal data sources based on whether they were publicly
available, in DNP or designated for inclusion in DNP, and included
information about entities. We did not include data sources that included
only individuals because our scope focused on entities. We also
considered whether the data sources contained information to support
Page 50 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
eligibility determinations. Specifically, we selected (1) GSA’s Federal
Audit Clearinghouse (FAC), (2) GSA’s System for Award Management
(SAM) Entity Registrations, (3) GSA’s SAM Exclusions, (4) HHS OIG’s
List of Excluded Individuals and Entities (LEIE), (5) IRS’s Automatic
Revocation of Exemption List (ARL), (6) IRS’s Form 990-N list, (7) IRS’s
Publication 78 list, (8) OFAC’s Specially Designated Nationals and
Blocked Persons (SDN) List, and (9) OFAC’s Non-SDN Lists. While
OFAC’s Non-SDN Lists have not specifically been designated for
inclusion in DNP, they are included in SAM Exclusions, which was
designated and is part of DNP. Therefore, we selected the Non-SDN Lists
as a data source for this work.
Because we made a nongeneralizable selection of data sources, our
findings for this objective cannot be used to make inferences about other
data sources. However, we determined that the selection of these data
sources was appropriate for our design and objectives and that the
selection would generate valid and reliable evidence to support our work.
For the nine selected data sources, we reviewed data dictionaries and
documentation of validation processes for consistency with
interoperability practices we identified and judgmentally selected. We
researched OMB guidance and our previous work and incorporated other
federal practices related to supporting and enabling data interoperability. 1
For example, OMB M-19-18 discusses the need to adopt or adapt, create
as needed, and implement data standards to maximize data quality and
to facilitate use, access, sharing, and interoperability. 2 The National
Institute of Standards and Technology emphasizes the importance of
structured data management and metadata, in which a data dictionary
plays a crucial role. However, OMB guidance and the National Institute of
Standards and Technology do not specify data standards or describe
what data standards should include. Therefore, we leveraged practices
included in the guidance on data dictionaries and metadata from the U.S.
1GAO, Spectrum IT Modernization: NTIA Should Fully Incorporate Cybersecurity and
Interoperability Practices, GAO-25-107509 (Washington, D.C.: May 22, 2025); Office of
Management and Budget, Open Data Policy-Managing Information as an Asset, OMB M-
13-13 (Washington, D.C.: May 9, 2013); and GAO, Federal Spending Transparency:
Opportunities Exist to Further Improve the Information Available on USAspending.gov,
GAO-22-104702 (Washington, D.C.: Nov. 8, 2021).
2Office of Management and Budget, Federal Data Strategy- A Framework for
Consistency, OMB M-19-18 (Washington, D.C.: June 4, 2019).
Page 51 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
Geological Survey and the documentation and validation rules from
Treasury in the Governmentwide Spending Data Model.
We assessed whether agencies documented data dictionaries and data
validation processes and their content based on practices identified in our
prior work, as well as data standards, policies, and processes that federal
agencies developed or use. 3 Specifically, we assessed whether data
dictionaries identified each data element by name and included its
definition and details, such as data type, character limits, and whether it is
required or optional. We also discussed these processes with relevant
agency officials to understand how the data are collected, validated,
maintained, and monitored.
We also performed electronic testing of data from the selected data
sources for fiscal years 2023 and 2024 (the latest data available at the
time of our analyses). We aimed to work exclusively with publicly
accessible data, which we determined reflects the type of information
agency officials would have, at a minimum, available for their decision-
making. We also attempted to align the date range for the selected data
sources for fiscal years 2023 and 2024. However, we could not always
align the date range for some of the data sources because of their
reporting periods and cadence. Specifically, for each of the data sources,
we obtained and used data as follows:
• FAC. On May 13, 2025, we downloaded public data from the FAC
application programming interface (API) with a FAC accepted date from
October 1, 2022, through September 30, 2024.
• SAM Entity Registrations. On August 29, 2024, we downloaded three
files from SAM.gov dated April 2, 2023, October 1, 2023, and April 7,
2024. On October 9, 2024, we downloaded the file dated October 6,
2024. According to the website, each file includes registrations that were
active as of the date of the file and inactive registrations for the previous
6 months. Because the dates of the files varied, there may have been
records not included in our data. Our data analysts had access to
additional information restricted to authorized users, such as Taxpayer
Identification Number (i.e., Social Security number or employer
3Sources for selected practices included GAO reports GAO-25-107509 and GAO, DATA
Act: OMB Needs to Formalize Data Governance for Reporting Federal Spending,
GAO-19-284 (Washington, D.C.: Mar. 22, 2019), and DATA Act: OMB and Treasury Have
Issued Additional Guidance and Have Improved Pilot Design but Implementation
Challenges Remain, GAO-17-156 (Washington, D.C.: Dec. 8, 2016); National Institute of
Standards and Technology guidance; and policies and processes federal agencies use,
such as the Governmentwide Spending Data Model.
Page 52 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
identification number). However, we did not use it for our data matching
tests because it is not publicly available.
• SAM Exclusions. On August 28, 2024, we downloaded four publicly
available files from SAM.gov dated November 2022, April 2023,
November 2023, and April 2024. On January 3, 2025, we downloaded
the file dated November 2024. 4 According to the website, each
semiannual extract file includes all active exclusions at the time the file is
generated. Because the as of date of the files is unclear, there may have
been records not included in our data.
• LEIE. On November 26, 2024, we downloaded from HHS OIG’s website
the monthly supplements files for January 2023 to September 2024. In
December 2024, HHS OIG officials provided us the files for October 2022
through December 2022 that were no longer available on the website. On
November 27, 2024, we downloaded from HHS OIG’s website the
October 2024 LEIE database along with the October 2024 monthly
supplement files documenting reinstatements and exclusions. Although
HHS OIG could have provided a single file for the period under our
review with additional information restricted to authorized users, such as
Taxpayer Identification Numbers, doing so would have required a
memorandum of understanding. We chose not to pursue this option as
we aimed to rely on publicly accessible data.
• ARL, the Form 990-N list, and the Publication 78 list. On December
10, 2024, we downloaded from the IRS website the bulk data files for
each of the three sources. According to IRS officials, all files were
updated as of December 9, 2024. Therefore, some records that might
have been included in the Form 990-N list and the Publication 78 list
during fiscal years 2023 and 2024 might not have been included by our
download date.
• SDN and Non-SDN Lists. On January 27, 2025, we downloaded OFAC
Extensible Markup Language files from the API. These data included
sanction records that were active as of the download date. Therefore, it
did not include records that might have been active during fiscal years
2023 and 2024 but subsequently changed to inactive prior to our
download date.
We designed some tests based on the specifications that the data source
owners established, such as acceptable values and data relationships.
We designed others based on our professional judgment to test for
apparent errors in accuracy and completeness, such as missing or invalid
4To minimize potential mismatches due to different download dates, we also downloaded
the SAM Exclusions data as of January 27, 2025, to match OFAC data.
Page 53 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
values, data formatting issues, and conflicting data relationships. For
verifying the validity of unique entity identifiers (UEI), we compared the
values against SAM entity information. 5
We used all data elements to check for complete duplicate records and
selected data elements to check for partial duplicates. For relevance, we
selected data elements to perform comparability tests based on factors
including whether agency documentation indicated that the elements
were required and DNP included the elements in search results. Selected
data elements generally included unique identifiers, name, address, and
relevant date information, to the extent available.
Additionally, we assessed the consistency and relationship between the
selected data sources by comparing them with each other. Without
accessible and reliable common unique identifiers, in our analyses, we
sometimes relied on individual and entity names or other fields, such as
country, to match records between data sources. These fields varied in
name, formatting, and number of fields, further complicating exact
matching. We generally used automated exact-matching procedures and
did not perform manual checks to determine whether additional or
different matches could have been identified. 6 We used exact matching
techniques, including a unique identifier when available, to replicate what
a human might do and avoid making incorrect matches. We did not use
“fuzzy matching”—a collection of techniques that calculate how similar the
values are and require human judgment to confirm a match. Fuzzy
matching without human judgment can reduce the accuracy and reliability
of the analysis by incorrectly identifying matches and missing true
matches. For instance, it can incorrectly identify matches, such as “Global
Tech Industries Inc” in one data source matching with “Global
Technologies Inc” in another data source.
We interviewed GSA officials, HHS OIG officials, and Treasury officials
from IRS and OFAC to understand the reasons for the issues we
identified in the data sources they own. We also interviewed officials from
5SAM Entity Registrations contains all active entity registrations and any registrations that
have expired in the prior 6 months. To obtain a population of all UEIs, we supplemented
the SAM Entity Registration records with the data for inactive registrations, ID assigned,
and unvalidated ID, but some records may not have been included in our population due
to differences in the download dates and system download limitations. We refer to the
supplemented records as SAM entity information.
6Our exact matching approach ignored spaces, punctuation, special characters, and
capitalization.
Page 54 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
the audit community who have worked with these data, such as the
Council of the Inspectors General on Integrity and Efficiency, and GSA
OIG, to gain an understanding of any data interoperability challenges they
may have identified. While we determined that the data we used from the
selected data sources were sufficiently reliable for the purposes of
assessing data interoperability, including accuracy and completeness, we
identified risks to the reliability of the data, as we discuss in the report.
To address objective three, we narrowed our selection to the data
sources that Treasury’s DNP categorized as debarment and had a UEI to
more closely identify potentially ineligible recipients. 7 We used only the
UEI data element to more reliably and accurately compare with the
USAspending.gov award data. 8 We selected GSA’s (1) SAM entity
information and (2) SAM Exclusions.
Because we made a nongeneralizable selection of data sources, our
findings for this objective cannot be used to make inferences about other
data sources nor the full population of awards. However, we determined
that the selection of these data sources was appropriate for our design
and objectives and that the selection would generate valid and reliable
evidence to support our work.
We downloaded all award transaction data for fiscal years 2023 and 2024
(the latest available at the time of our analyses) from the
USAspending.gov Award Data Archive on February 3, 2025. We then
compared SAM entity information records with the USAspending.gov
contract and financial assistance award data for fiscal years 2023 and
2024 to determine if the recipient UEIs in USAspending.gov were valid.
To identify awards made to excluded UEIs during the exclusion period,
we analyzed the award transaction dates in relation to the corresponding
exclusion dates to determine if a UEI was excluded at the same time as
the transaction. Because contracts have transaction identifiers for
7In February 2026, Treasury revised the DNP website and database categories. For
example, previously Treasury categorized GSA’s SAM entity information and SAM
Exclusions as debarment. These are currently categorized as Entity and Disallowed,
respectively. The UEI is an identification number that SAM.gov assigns to entities doing
business with the federal government.
8USAspending.gov data contain the UEI data element and do not contain the employer
identification number or National Provider Identifier information. Of the previously selected
nine data sources, GSA’s SAM Entity Registrations, SAM Exclusions, and FAC include
the UEI data element. However, we excluded FAC from this analysis, because it was not
relevant for either validating a UEI value in USAspending.gov or determining if the
recipients are debarred (prohibited) from receiving federal awards.
Page 55 GAO-26-107466 Federal Data Interoperability
Appendix I: Objectives, Scope, and
Methodology
closeout, legal contract cancellation, terminate for cause, terminate for
default, and terminate for convenience, we excluded these types of
transactions from our matches. We did not make a similar exclusion for
financial assistance transactions because the identifier did not have
categories to indicate termination.
While each award generally has multiple transactions throughout its life
cycle, we summarized the matches by award for reporting purposes. For
federal award recipient UEIs in USAspending.gov that matched a UEI on
SAM Exclusions, we summarized the number of contract and financial
assistance awards, and their associated total obligation amounts for fiscal
years 2023 and 2024, to quantify the magnitude of awards made to
potentially ineligible federal award recipients. We also downloaded SAM
Exclusions web search data in April 2026 to verify UEIs for some
exclusion records that did not match SAM entity information. While we
determined that the data we used from the selected data sources were
sufficiently reliable for the purpose of matching exclusions data and
awards, we identified risks to the reliability of the data, as we discuss in
the report. For USAspending.gov, we assessed the reliability of award
data by checking completeness of the UEIs and award identifiers, as well
as the validity of the UEIs by comparing them to SAM entity information.
We determined that data we used from USAspending.gov were
sufficiently reliable for the purpose of matching exclusions data and
awards, and we discuss data reliability issues in the report.
We conducted this performance audit from March 2024 to June 2026 in
accordance with generally accepted government auditing standards.
Those standards require that we plan and perform the audit to obtain
sufficient, appropriate evidence to provide a reasonable basis for our
findings and conclusions based on our audit objectives. We believe that
the evidence obtained provides a reasonable basis for our findings and
conclusions based on our audit objectives.
Page 56 GAO-26-107466 Federal Data Interoperability
Appendix II: Publicly Available File Formats Appendix II: Publicly Available File Formats of
the Selected Data Sources
of the Selected Data Sources
Table 11 provides the file formats for the publicly available data sources
selected in our review.
Table 11: Publicly Available File Formats of the Selected Data Sources
Publicly available file formats
Owner Pipe
Data source CSV Fixed-field JSON PDF delimited text XML
General Services Administration
Federal Audit Clearinghouse P P
System for Award Management P P P
(SAM) Entity Registrations
SAM Exclusions P P
Department of Health and Human Services - Office of Inspector General
List of Excluded Individuals and P
Entities
Department of the Treasury - Internal Revenue Service
Automatic Revocation of P
Exemption List
Form 990-N list P
Publication 78 list P
Treasury - Office of Foreign Assets Control (OFAC)
Specially Designated Nationals P P P Pa
and Blocked Persons (SDN) List
Non-SDN Consolidated Sanctions P P P Pa
List
Legend: P = The data source is publicly available in the indicated file format; CSV = Comma-Separated Values; JSON = JavaScript Object Notation;
PDF = Portable Document Format; and XML = Extensible Markup Language.
Source: GAO analysis of selected data sources. | GAO-26-107466
OFAC publishes standard, advanced, and enhanced versions of XML files.
a
Page 57 GAO-26-107466 Federal Data Interoperability
Appendix III: Comments from the Bureau of
Appendix III: Comments from the Bureau of the
Fiscal Service
the Fiscal Service
Page 58 GAO-26-107466 Federal Data Interoperability
Appendix III: Comments from the Bureau of the
Fiscal Service
Page 59 GAO-26-107466 Federal Data Interoperability
Appendix IV: GAO Contact and Staff
Appendix IV: GAO Contact and Staff
Acknowledgments
Acknowledgments
Paula M. Rascona, rasconap@gao.gov
GAO Contact
In addition to the contact named above, María C. Belaval, Mariana
Staff Calderón, and Sarah Hay (Assistant Directors); Erika Szatmari (Auditor in
Acknowledgments Charge); Giovanna K. Cruz; Joanne Howard; Jason Kirwan; Cory A.
Mazer; Brenda Mittelbuscher; Gabriel M. Nelson; Christian P. Sullano;
and Lisa Zhao made key contributions to this report.
Page 60 GAO-26-107466 Federal Data Interoperability
Related GAO Products
Related GAO Products
Federal Spending Transparency: Actions Needed to Help Ensure
Procurement Data Quality. GAO-25-107469. Washington, D.C.:
September 25, 2025.
Grants Management: Recent Guidance Could Enhance Subaward
Oversight. GAO-25-107315. Washington, D.C.: March 26, 2025.
Program Integrity: Agencies and Congress Can Take Actions to Better
Manage Improper Payments and Fraud Risks. GAO-25-108172.
Washington, D.C.: March 11, 2025.
Payment Integrity: Significant Improvements Are Needed to Address
Improper Payments and Fraud. GAO-24-107660. Washington, D.C.:
September 10, 2024.
Fraud Risk Management: 2018-2022 Data Show Federal Government
Loses an Estimated $233 Billion to $521 Billion Annually to Fraud, Based
on Various Risk Environments. GAO-24-105833. Washington, D.C.: April
16, 2024.
Grants Management: Action Needed to Ensure Consistency and
Usefulness of New Data Standards. GAO-24-106164. Washington, D.C.:
January 25, 2024.
Federal Spending Transparency: Opportunities to Improve
USAspending.gov Data. GAO-24-106214. Washington, D.C.: November
7, 2023.
Federal Spending Transparency: OIGs Identified a Variety of Issues with
the Quality of Agencies’ Data Submissions. GAO-22-105427.
Washington, D.C.: July 12, 2022.
Emergency Relief Funds: Significant Improvements Are Needed to
Ensure Transparency and Accountability for COVID-19 and Beyond.
GAO-22-105715. Washington, D.C.: March 17, 2022.
Federal Spending Transparency: Opportunities Exist for Treasury to
Further Improve USAspending.gov’s Use and Usefulness.
GAO-22-104127. Washington, D.C.: December 16, 2021.
Federal Spending Transparency: Opportunities Exist to Further Improve
the Information Available on USAspending.gov. GAO-22-104702.
Washington, D.C.: November 8, 2021.
Page 61 GAO-26-107466 Federal Data Interoperability
Related GAO Products
DATA Act: OIGs Reported That Quality of Agency-Submitted Data
Varied, and Most Recommended Improvements. GAO-20-540.
Washington, D.C.: July 9, 2020.
DATA Act: Quality of Data Submissions Has Improved but Further Action
Is Needed to Disclose Known Data Limitations. GAO-20-75. Washington,
D.C.: November 8, 2019.
DATA Act: OMB Needs to Formalize Data Governance for Reporting
Federal Spending. GAO-19-284. Washington, D.C.: March 22, 2019.
DATA Act: Reported Quality of Agencies’ Spending Data Reviewed by
OIGs Varied Because of Government-wide and Agency Issues.
GAO-18-546. Washington, D.C.: July 23, 2018.
DATA Act: OMB, Treasury, and Agencies Need to Improve Completeness
and Accuracy of Spending Data and Disclose Limitations. GAO-18-138.
Washington, D.C.: November 8, 2017.
DATA Act: As Reporting Deadline Nears, Challenges Remain That Will
Affect Data Quality. GAO-17-496. Washington, D.C.: April 28, 2017.
DATA Act: Office of Inspector General Reports Help Identify Agencies’
Implementation Challenges. GAO-17-460. Washington, D.C.: April 26,
2017.
DATA Act: Implementation Progresses but Challenges Remain.
GAO-17-282T. Washington, D.C.: December 8, 2016.
DATA Act: OMB and Treasury Have Issued Additional Guidance and
Have Improved Pilot Design but Implementation Challenges Remain.
GAO-17-156. Washington, D.C.: December 8, 2016.
Improper Payments: Strategy and Additional Actions Needed to Help
Ensure Agencies Use the Do Not Pay Working System as Intended.
GAO-17-15. Washington, D.C.: October 14, 2016.
DATA Act: Improvements Needed in Reviewing Agency Implementation
Plans and Monitoring Progress. GAO-16-698. Washington, D.C.: July 29,
2016.
Page 62 GAO-26-107466 Federal Data Interoperability
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DATA Act: Progress Made but Significant Challenges Must Be Addressed
to Ensure Full and Effective Implementation. GAO-16-556T. Washington,
D.C.: April 19, 2016.
DATA Act: Data Standards Established, but More Complete and Timely
Guidance Is Needed to Ensure Effective Implementation. GAO-16-261.
Washington, D.C.: January 29, 2016.
DATA Act: Progress Made in Initial Implementation but Challenges Must
be Addressed as Efforts Proceed. GAO-15-752T. Washington, D.C.: July
29, 2015.
Page 63 GAO-26-107466 Federal Data Interoperability
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