Federal Data Management: Issues and Challenges in the Use of Data Standards

April 29, 2024 R48053

Federal Data Management: Issues and
April 29, 2024
Challenges in the Use of Data Standards
Natalie R. Ortiz
The federal government manages a significant amount of data. Congress expects federal agencies
Analyst in Government
to be effective managers and stewards of the data with which they are entrusted. Sometimes,
Organization and
Congress contributes to this stewardship by enacting policies that require data standards. Data
Management
standards can be found in laws concerning a wide array of congressional matters and interests.

When Congress requires data standards, it is essentially requiring some degree of data
governance and management. In the most general sense, data standards create rules for data in

some way and, when used, could contribute to the usability of data by agencies, including to their
consistency, transparency, discoverability, reliability, accessibility, and quality. Data standards may also reduce
administrative burdens, assist in extracting value from the federal investment in information technology (IT), support federal
data integration and interoperability, and contribute to the use of emerging technologies, such as artificial intelligence.
Data standards are numerous and varied, and data can be subjected to standards in many ways. Various types of data
standards appear in the law, such as open data standards, data format standards, and data exchange standards. Some of these
types can be interpreted in practice to have multiple meanings, and some standardize data in some ways but not necessarily in
others, which can affect the extent to which they serve Congress’s intended policy goals. There are also differences among
agencies in how they define and describe what data standards do independent of a legislative mandate to use them. Put
simply, there is a no single way to approach data standards, which can complicate their effective use. Thus, specifying data
standards in the law can pose a challenge to lawmakers. Within agencies, chief data officers (CDOs) have a statutory
responsibility for data management. If Congress identifies a role for data standards in future legislation—whether for specific
programs, regulatory matters, or government-wide operations—it may consider ensuring that CDOs have a role in
implementing these data standards and are sufficiently resourced to manage the use of data standards within agencies and the
amount of data that agencies oversee.
Data standards operate within separate policy frameworks for information resource management and technical standards.
Technical standards establish specifications for products or production methods, and data standards for federal purposes have
largely been viewed as being under the umbrella of technical standards. Federal policymakers have generally preferred to use
voluntary consensus standards—that is, technical standards developed by the private sector—in lieu of standards developed
by the federal government for its own unique purposes. Voluntary consensus standards are not always available and, in some
cases, may fail to serve agencies’ data-related needs. While agencies receive some guidance on implementing data standards
from the Office of Management and Budget and the National Institute of Standards and Technology, this guidance and the
underlying policy frameworks may require further coordination to help promote the effective management and use of data
standards by agencies.
Congress has at times sought to enable data interoperability by enacting laws that require data standards. Some of the greatest
challenges to achieving interoperability concern the coordination of people, organizations, and processes. Relying on data
standards alone will probably be insufficient to address some of these barriers to data interoperability. Congress may consider
including frameworks for data governance in policies where data interoperability is a goal.
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Federal Data Management: Issues and Challenges in the Use of Data Standards

Introduction
The federal government might be one of the largest producers of data in the world.1 Congress
entrusts federal agencies to be effective stewards of these data. Congress has a broad and long-
standing interest not only in how agencies use data in day-to-day operations but in ensuring the
judicious management of federal data. Data standards may enable federal data to be managed
more effectively. When federal data are subject to data standards, there is an expectation for those
data to conform to or comply with the standards’ requirements. Conformity to those requirements
should then send a signal about the readiness and usability of the data for a particular purpose.
For this report, federal data means data collected, processed, maintained, disseminated, managed,
or regulated by a federal agency, including that which are reported to a federal agency. The Office
of Management and Budget (OMB) has characterized the influence of federal data in the
following way:
Federal data drives the U.S. economy and civic engagement and there is virtually no policy
or program decision facing a federal agency that would not benefit from the use of data.2
Consistent with this influence, the public also expects agencies to effectively produce federal
data. For example, the Commerce Department states that more than 30 million U.S. businesses,
325 million Americans, and 93,000 tribal, state, and local governments rely on its data to make
informed decisions.3
Federal lawmakers sometimes require data standards for the data that agencies are entrusted to
manage. Requirements for data standards can be found in laws concerning a variety of
congressional matters and interests, including federal spending, financial regulation, homeland
security, public land use, transportation, environmental protection and conservation, public
welfare, health care, and others. While data standards generally establish rules for data, there are
various data standards and various ways data could be subjected to standards. What exactly data
standards require for data may largely depend on context and implementation, which includes
how data standards have been specified by lawmakers within any given law.
Data standards can be used for an array of purposes, factoring into how federal agencies use data
in various operations and processes, including in the management of various federal programs.
For example, these standards might set requirements for what data are needed at a minimum to
automate a payment process,4 what “primary place of performance” is to mean for all agencies
when reporting on federal financial awards,5 what data certain users of an information system can
access, or how data are to be structured or formatted to electronically transmit data across
information systems using a particular machine-readable format.
This report provides an overview of data standards for federal data, focusing primarily on their
relationship to data management. It discusses a number of issues in using data standards,

1 Bill Brantley, “The Value of Federal Data,” March 18, 2018, https://digital.gov/2018/03/14/data-briefing-value-
federal-government-data/.
2 OMB, M-19-18: Federal Data Strategy—A Framework for Consistency, June 4, 2019, p. 1,
https://www.whitehouse.gov/wp-content/uploads/2019/06/M-19-18.pdf.
3 Department of Commerce, “America’s Data Agency,” https://www.commerce.gov/tags/americas-data-agency.
4 This example draws from the Federal Integrated Business Framework. See more at General Services Administration
(GSA), “Standard Data Elements—Financial Management,” https://ussm.gsa.gov/fibf-fm/#standard_data_elements.
5 This example is from the implementation of the data standards required by the Digital Accountability and
Transparency Act (P.L. 113-101). For additional context, see Government Accountability Office, “DATA Act,”
https://www.gao.gov/assets/680/674866.pdf.
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including how to define data standards and the consequences of inadequate data standards
management on the usability of federal data. Various types of data standards appear in the law—
such as open data standards and data exchange standards—but such types can be loose
constructions in practice and interpreted in various ways. Some of the ways data standards have
been specified, implemented, and used in federal policymaking activities are discussed below.
This report also discusses the multiple policy frameworks for data standards. OMB and the
National Institute of Standards and Technology (NIST) provide guidance to agencies on using
data standards within these frameworks. The report concludes with some considerations for
Congress when it seeks to require data standards for federal data, including the specification of
data standards, data standards within specific policies, data standards for federal data
government-wide, and federal data interoperability.6
Using Data Standards: Benefits and Challenges
Congress may include data standards in laws to ensure that federal data are useful. Independent of
a legislative mandate, agencies may also use data standards to increase the usefulness of the
federal data they manage. Where there is a desire within Congress to use data to inform its own
efforts to develop policy, data standards may have a role in ensuring that data from executive
branch agencies are useful for the lawmaking process. This section begins with a discussion of
data standards as a data governance activity and some of the ways that data standards contribute
to the usefulness of federal data according to these legislative interests and administrative efforts.
Following a discussion of the benefits of using data standards, some of the challenges to using
them are described.
Benefits
When policymakers create requirements for data standards in a law, they are expecting some
degree of data governance and management. In a report that stems from several statutory
requirements for it to report on the quality of certain federal data and on certain efforts related to
federal data management, the Government Accountability Office (GAO) states that an effective
data governance framework for federal data allows agencies to improve performance and
outcomes.7 Data governance includes the authorities, roles, responsibilities, organizational
structures, policies, procedures, standards, and resources for the definition, stewardship,
production, security, and use of data.8 GAO suggests that data governance is distinct from but a
precursor to effective data management, because the former is concerned with establishing
mechanisms for decisionmaking while the latter is concerned with the implementation of those
decisions.9

6 In the context of information technology, interoperability generally refers to the ability of data from one system to be
used by another system. The emphasis in interoperability is on the extent to which data is ready for use following data
exchange. Some laws have defined interoperability for a specific purpose (e.g., E-Government Act of 2002 [P.L. 107-
347]), while other laws use the term without a definition (e.g., Paperwork Reduction Act of 1995 [P.L. 104-13]).
7 GAO, Data Governance: Agencies Made Progress in Establishing Governance, but Need to Address Key Milestones,
GAO-21-152, December 2020, p. 7, https://www.gao.gov/assets/gao-21-152.pdf#page=12.
8 GAO, Data Governance, pp. 4-6.
9 In a somewhat different construction, GSA suggests that data governance is the practice of data management (see
GSA, “Ten Plays of Our Data and Analytics Approach,” October 2020, https://coe.gsa.gov/2020/10/19/da-update-
9.html#Key%20Concepts). One industry-based group describes data governance as the exercise of authority and
control over the management of data assets and states that all organizations make such decisions regardless of whether
(continued...)
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GAO states that data governance ensures that data are transparent, accessible, and of sufficient
quality for their intended use.10 In a separate, congressionally mandated report, GAO identifies
data standards as “a recognized approach for increasing the consistency, and therefore the
transparency, of data,” contributing to completeness, accuracy, and usefulness.11 GAO also
reported that data standards could reduce burden by minimizing inconsistent and duplicative
reporting requirements. While Congress has a general interest in reducing administrative burdens
on the public, including paperwork burdens, data standards may result in efficiencies for agencies
and allow agencies to meaningfully aggregate or compare data that is otherwise difficult in the
absence of data standards.
Congress sometimes requires data standards for certain government-wide activities and
operations, in part to promote the transparency of federal data. The Digital Accountability and
Transparency Act of 2014 (DATA Act), for example, required data standards for the financial data
reported by executive branch agencies, in part to produce reliable government-wide spending data
on USAspending.gov.12 Title II of the Foundations for Evidence-Based Policymaking Act of 2018
(FEBPA)—known as the Open, Public, Electronic, and Necessary (OPEN) Government Data
Act—requires OMB to develop guidance for using metadata to describe agency datasets in
comprehensive data inventories.13 This guidance could include using metadata standards that are
consistent with administrative practices that the act sought to codify.14 Under the OPEN
Government Data Act, agencies must make certain agency datasets and the corresponding
metadata available to the public.15 In addition to making federal data discoverable to the public,
the transparency of datasets maintained by a federal agency may also assist other federal agencies

data governance is a formalized function (see DAMA International, DAMA-DMBOK: Data Management Book of
Knowledge, 2nd ed. [Basking Ridge, NJ: Technics Publications, 2017], p. 67). GAO’s definition of data governance
may suggest more formalized functions.
10 GAO, Data Governance, p. 4.
11 GAO, Grants Management: Action Needed to Ensure Consistency and Usefulness of New Data Standards, GAO-24-
106164, January 2024, pp. 4, https://www.gao.gov/assets/d24106164.pdf#page=9.
12 One of the purposes of the DATA Act is to “establish Government-wide data standards for financial data and provide
consistent, reliable, and searchable Government-wide spending data that is displayed accurately for taxpayers and
policy makers on USAspending.gov (or a successor system that displays the data)” (P.L. 113-101, §2(2); 128 Stat.
1146).
13 P.L. 115-435, §202(d); 132 Stat. 5538. Comprehensive in this context includes datasets created by, collected by,
under the control of, at the direction of, or maintained by an agency. The inventory is supposed to provide a clear and
comprehensive understanding of the datasets in the possession of an agency (see 44 U.S.C. §3511(a)(1-2)). The act
defines and uses the term data assets to mean “a collection of data elements or data sets that may be grouped together”
(44 U.S.C. §3502(17)).
14 This codification is discussed in U.S. Congress, House Committee on Oversight and Government Reform,
Foundations for Evidence-Based Policymaking Act of 2017, report to accompany H.R. 4174, H.Rept. 115-411, 115th
Cong., 1st sess., pp. 11-12, https://www.congress.gov/115/crpt/hrpt411/CRPT-115hrpt411.pdf#page=11. The
committee report discusses Executive Order 13642, “Making Open and Machine Readable the New Default for
Government Information” (available at https://www.govinfo.gov/content/pkg/FR-2013-05-14/pdf/2013-11533.pdf) and
at OMB, M-13-13: Open Data Policy—Managing Information as an Asset, May 9, 2013, https://www.whitehouse.gov/
wp-content/uploads/legacy_drupal_files/omb/memoranda/2013/m-13-13.pdf. This memorandum introduced the use of
metadata standards. OMB identifies these standards by the name “Project Open Data Metadata Schema.” Information
about these standards (schema) can be found at https://resources.data.gov/resources/dcat-us/ and are also discussed
elsewhere in this report.
15 P.L. 115-435, §202(d); 132 Stat. 5539; 44 U.S.C. §3511(a)(2)(C-D). For additional information on the OPEN
Government Data Act, see CRS In Focus IF12299, The OPEN Government Data Act: A Primer, by Meghan M.
Stuessy.
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in determining whether collecting data from the public or purchasing data may duplicate data
available elsewhere within the federal government.16
Congress and the policymaking process may also benefit from data standards. In the 118th
Congress, H.Con.Res. 49 would establish a commission within the legislative branch that would
study and recommend how Congress might use “real-time, structured, integrated, and machine-
readable” data in lawmaking.17 Use of data in this way for Congress’s needs may depend on a
number of data management activities that occur within and across agencies, such as the effective
use of data standards.
Administratively, some agencies have signaled their plans to use data standards in their agency-
wide, or enterprise, data strategies.18 For example, the Department of State stated in its 2021 data
strategy that it would transform how it collectively manages and uses data across its mission
areas. The department identified defining and implementing data standards as part of a goal to
establish mission-driven data management, saying, “An enterprise approach to data standards is
needed, as current approaches are bespoke to specific data products and are not applied uniformly
nor broadly understood…. The standards will enable greater discovery, utility, security, and
efficacy of the department’s data.”19
Data standards for federal data are also relevant to emerging technologies affecting federal
government operations, particularly artificial intelligence (AI). NIST characterized data standards
as making the training data needed for machine learning applications more visible to users and
more usable, assisting in creating “effective, reliable, robust, and trustworthy AI technologies.”20
In this case, data standards are viewed as “measuring and sharing information relating to the
quality, utility, and access of datasets … assist[ing] potential users in making informed decisions
about the data’s applicability to their purpose, and help[ing] prevent misuse.” OMB’s guidance to
agencies indicates that any data used to develop, test, or maintain AI applications should be
assessed for quality and other features and that reducing barriers to data for training, testing, and
operating AI should be supported by resources that enable data governance and management
practices, particularly in data collection, curation, labeling, and stewardship.21

16 The Paperwork Reduction Act of 1995 (PRA; P.L. 104-13) requires agencies to complete certain steps before
collecting information from the public, including receiving OMB’s approval of the information collection (44 U.S.C.
§3507(a)). OMB requires agencies to provide certifications of certain criteria relevant to the information collection,
including that it “is not unnecessarily duplicative of information otherwise reasonably accessible to the agency” (5
C.F.R. §1320.9(b)). For more information on the PRA’s information collection requirements, see CRS In Focus
IF11837, The Paperwork Reduction Act and Federal Collections of Information: A Brief Overview, by Maeve P. Carey
and Natalie R. Ortiz.
17 H.Con.Res. 49 §2(g)(1)(E).
18 For example, see National Aeronautics and Space Administration, NASA Data Strategy, January 2021, p. 18,
https://www.nasa.gov/wp-content/uploads/2023/02/nasa_data_strategy.pdf#page=20; see also Department of Labor,
Enterprise Data Strategy, April 2022, p. 8, https://www.dol.gov/sites/dolgov/files/Data-Governance/DOL-Enterprise-
Data-Strategy-2022.pdf#page=10.
19 Department of State, Enterprise Data Strategy: Empowering Data Informed Diplomacy, September 2021, p. 14,
https://www.state.gov/wp-content/uploads/2021/09/Reference-EDS-Accessible.pdf#page=14.
20 NIST, U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tools,
August 9, 2019, p. 13, https://www.nist.gov/system/files/documents/2019/08/10/
ai_standards_fedengagement_plan_9aug2019.pdf#page=15.
21 OMB, M-24-10: Advancing Governance, Innovation, and Risk Management for Agencies, , March 28, 2024, p. 11,
https://www.whitehouse.gov/wp-content/uploads/2024/03/M-24-10-Advancing-Governance-Innovation-and-Risk-
Management-for-Agency-Use-of-Artificial-Intelligence.pdf#page=11.
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Issues in Defining Data Standards
OMB, the General Services Administration (GSA), and the National Archives and Records
Administration (NARA)—which jointly maintain online tools, guidance, and other resources for
managing and using federal data—have characterized the universe of data standards as “large,
varied, and complex” and have stated there is no single definition for data standards for federal
data management purposes.22
Researchers have found that practitioners sometimes disagree about concepts fundamental to data
standards.23 Uncertainty in fundamentals might be a challenge when data standards often require
coordination among different groups. Some have suggested that effectively implementing data
standards requires different groups to understand data standards, find a shared vocabulary, and
figure out collectively how to develop and use data standards that serve related but not identical
goals.24
Federal agencies differ in how they define data standards. For example, the Environmental
Protection Agency defines data standards as “documented agreements on representation, format,
definition, structuring, tagging, transmission, manipulation, use, and management of data.”25 The
U.S. Geological Survey states that data standards operate at the “parameter-level” and “dataset-
level.”26 As part of their collective effort, OMB, GSA, and NARA define data standards as a
hierarchy of concepts while also noting the lack of formal definitions for the concepts within this
hierarchy (see text box “Data Standards as a Hierarchy of Concepts”). Thus, this hierarchy also
represents just one interpretation of data standards that may not be widely used outside of these
agencies or are termed differently in practice.
Data Standards as a Hierarchy of Concepts
GSA, OMB, and NARA characterize data standards as often being comprised of “smaller component pieces,”
“interchangeable parts,” or “common building blocks” that can be mixed and matched for different purposes. In
this way, the agencies break down data standards into a “hierarchy of concepts” with each building on the
previous:
•
Data standard components. Data standards are typically made up of discrete “components.”
Components of a data standard can include specification for (1) data type; (2) identifiers; (3) vocabulary, such
as terms and definitions; (4) data models, schemas, and other representations that define relationships among
pieces of information; (5) data format; and (6) protocols for reading and writing data within a file system or
database or across computer systems and networks.
•
Data standards package. Multiple components can be assembled together, creating a more
comprehensive “data standards package.” These packages provide the instructions on how to implement the
individual components. The agencies note that the exact terminology for “data standards package” varies.

22 OMB, GSA, and NARA, “Data Standards Concepts and Definitions,” https://resources.data.gov/standards/concepts/.
23 Boris Otto, Erwin Folmer, and Verena Ebner, “A Characteristics Framework for Semantic Information Systems
Standards,” Information Systems and e-Business Management, vol. 10, no. 4 (December 2012), p. 573.
24 Richard Berner and Kathryn Judge, The Data Standardization Challenge, European Corporate Governance Institute,
ECGI Working Paper Series in Law no. 438/2019, January 2019, p. 12.
25 Environmental Protection Agency, “Learn About Data Standards,” https://www.epa.gov/data-standards/learn-about-
data-standards#What.
26 U.S. Geological Survey, “Data Standards,” https://www.usgs.gov/data-management/data-standards. The agency
describes dataset-level standards as specifying the “scientific domain, structure, relationships, field labels, and
parameter-level standards for the dataset as a whole.” These dataset-level standards are normally documented in a data
dictionary. Parameter-level standards specify “the format and units for a given parameter or field within a dataset and
help users correctly interpret the values” and “should be adopted at the time of data collection … when values in a field
are created or recorded.”
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•
Data standards framework. The agencies describe a data standards framework as a “comprehensive
system of reusable data standards components” that allow individual components to be combined in various
ways in order to serve a range of use cases.
GSA, OMB, and NARA note that their concepts do not represent formal definitions but are generally used to
describe common concepts associated with data standards. As such, it may be difficult to directly apply these
concepts and how they relate to each other in other contexts.
Additionally, the agencies note that standard itself has two meanings, one that is used in government to refer to a
requirement, a compliance measure, or a minimum set of qualification criteria that something must meet and a
second that is used in digital technology to refer to a common technical specification for how information is
described, processed, or transmitted. For their purposes, GSA, OMB, and NARA use standard according to this
second meaning, because “data standards are not intended to describe minimum qualification criteria that data
should meet, but instead describe technical specifications that allow for the consistent and interoperable col ection
and exchange of data in specific environments.”27
GAO differentiates between data standards that are used by programs or individual agencies and
government-wide data standards.28 In the former, an agency or program may use agreed-upon
definitions and technical specifications of data elements that may be different from those used at
another agency or for another program. For example, different agencies could use the same name
for a data element (e.g., address), but that data element may actually represent different things at
each agency (e.g., mailing address versus residential address). In contrast, government-wide data
standards attempt to consistently define and specify data elements across agencies or programs.
Some laws require specific types of data standards (e.g., data exchange standards, data format
standards, or open data standards). In practice, these types of data standards also do not have a
single, universally applied definition. Different interpretations of data standards may lead to
differences in their implementation. Some data standards that appear in the law and the various
ways such standards can be interpreted and have been implemented are discussed in a later
section of this report (see “Types of Data Standards in Federal Laws”).
Inadequate Data Standards Management and Data Governance
Data standards for federal data may require deliberate and active management to contribute to
their intended purposes. Inadequate management of data standards has real consequences on the
reliability, discoverability, and usability of federal data.
GAO has found shortcomings in the data that appear on USAspending.gov, which is supposed to
be an authoritative source of federal spending information for the public and policymakers.29 It
found that the federal financial data standards developed by OMB and the Treasury Department
pursuant to the DATA Act were not uniformly interpreted or applied by agencies, resulting in
reporting differences that affect the reliability of information on USAspending.gov.30 GAO, thus,
recommended the consistent use of federal financial data standards to ensure the integrity of the
standards over time. Specifically, GAO reported that a properly implemented formal data
governance structure could help adjudicate revisions to the data standards, ensure compliance

27 OMB, GSA, and NARA, “Data Standards Concepts and Definitions.”
28 GAO, Grants Management: Action Needed to Ensure Consistency and Usefulness of New Data Standards, p. 4.
29 GAO has published several reports on the data available through USAspending.gov. GAO provides a list of some of
these reports, in addition to other relevant reports, in GAO, Federal Spending Transparency: Opportunities Exist to
Further Improve the Information Available on USAspending.gov, GAO-22-104702, November 2021, pp. 59-61,
https://www.gao.gov/assets/d22104702.pdf#page=65.
30 GAO, DATA Act: Quality of Data Submissions Has Improved but Further Action Is Needed to Disclose Known Data
Limitations, GAO-20-75, November 2019, p. 27, https://www.gao.gov/assets/gao-20-75.pdf#page=33.
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with the data standards, and increase the accuracy of the federal financial data made available to
the public.31
There is also some indication of inadequate management of metadata standards agencies use for
making data available to the public. The Federal Chief Data Officers (CDO) Council
recommended that OMB establish governance of the metadata standards it requires agencies to
use to catalogue federal data pursuant to the OPEN Government Data Act.32 In 2013, OMB
introduced metadata standards—which OMB called a schema—in association with an
administrative effort to manage federal data that would support their discoverability and usability
by the public.33 The underlying metadata standards have since been updated by their
nongovernmental developers, who stated that the update was necessary because “the original
specification lacked a number of essential features.”34 The council recommended that OMB adopt
the updated metadata standards because they were more applicable to federal agencies and their
constituents, improving dataset “search, discovery, and appropriate use.”35 Additionally, some
stakeholders believe that the current metadata standards for open federal data “will not capture
the rich detail” needed for adequate transparency for federal statistical data products but are still a
“useful starting point” for other federal data products, in part because most federal agencies have
started to use the existing standards.36
Data Standards in Context: Frameworks and Types
Data standards for federal data operate within policy frameworks for federal information
management and the use of technical standards by agencies. Federal information management
policies have evolved to specifically include data management and a role for CDOs to oversee
data management within agencies. Within technical standards policy, there is preference for
agencies to use voluntary consensus standards over other types of technical standards.
Information Management
One framework for using data standards includes the Paperwork Reduction Act (PRA)—codified
at Title 44, Sections 3501-3521, of the U.S. Code—which governs the collection, processing, and
management of federal data by agencies.37 Among several other purposes, the PRA was designed
to improve the quality and use of federal data and to minimize the federal cost of collecting,
managing, and using federal data.38 Through the amendments made by FEBPA to the U.S. Code

31 GAO, DATA Act: OMB Needs to Formalize Data Governance for Reporting Federal Spending, GAO-19-284, March
2018, p. 4, https://www.gao.gov/assets/gao-19-284.pdf#page=8.
32 Federal CDO Council Data Inventory Working Group, Enterprise Data Inventories, April 2022, p. 20,
https://resources.data.gov/assets/documents/CDOC_Data_Inventory_Report_Final.pdf#page=24. Requirements for a
federal data catalogue are specified in Title 44, Section 3511(c), of the U.S. Code.
33 OMB, M-13-13, p. 5.
34 Riccardo Albertoni et al., eds., “Data Catalog Vocabulary (DCAT)—Version 2,” World Wide Web Consortium,
February 4, 2020, https://www.w3.org/TR/vocab-dcat-2/#motivation.
35 Federal CDO Council Data Inventory Working Group, Enterprise Data Inventories, April 2022, p. 20,
https://resources.data.gov/assets/documents/CDOC_Data_Inventory_Report_Final.pdf#page=24.
36 National Academies of Sciences, Engineering, and Medicine, Transparency in Statistical Information for the
National Center for Science and Engineering Statistics and All Federal Statistical Agencies (Washington, D.C.:
National Academies Press, 2022), p. 63, available for download at https://doi.org/10.17226/26360.
37 Originally enacted as the Paperwork Reduction Act of 1980 (P.L. 96-511), it was amended by the Paperwork
Reduction Act of 1995 (P.L. 104-13).
38 P.L. 104-13, §2; 109 Stat. 164; 44 U.S.C. §3501(4-5).
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provisions codifying the PRA, Congress has set some expectations for federal data management
and for the use of data standards for federal data.
The PRA establishes government-wide and agency roles and responsibilities for several different
aspects of information resource management (IRM). The PRA defines IRM as the “process of
managing information resources to accomplish agency missions and to improve agency
performance, including through the reduction of information collection burdens on the public.”39
Some have noted that while many observations of the PRA are primarily concerned with how it
governs the collection of information from individuals, businesses, and other entities, the act’s
scope is to improve management and efficiency in the federal government.40
Data Management
Federal agencies’ use of data standards is closely linked with information technology (IT) and
information systems. A congressional committee report on the PRA noted that one intent of the
PRA’s IRM requirements was “to refocus the attention of federal managers on the pressing need
to use [IT] to support programs efficiently and effectively:”
The reduction of public paperwork burdens will also be served by the legislation’s other
management focus. The still widening gap between possibilities for improved government
operations through the use of information technology, and the government’s apparent
inability to take advantage of this technology, demonstrates that the [Paperwork Reduction
Act of 1980]’s IRM mandates have not been sufficiently realized. Today’s information
systems offer the government unprecedented opportunities to provide higher quality
services tailored to the public’s changing needs, delivered more effectively, faster, at lower
cost, and with reduced burdens on the public. Unfortunately, Federal agencies have not
kept pace with evolving management practices and skills necessary to: (1) precisely define
critical information needs; and (2) select, apply, and manage changing information
technologies. The result, in many cases, has been wasted resources, a frustrated public
unable to get quality service, and a government ill-prepared to measure and manage its
affairs in an acceptable manner. Despite spending more than $200 billion on information
management and systems in the past 12 years, the government has too little evidence of
meaningful returns. The consequences—poor service quality, high costs, low productivity,
unnecessary risks and burdens, and unexploited opportunities for improvement—cannot
be tolerated.41
Despite the link, the House Committee on Oversight and Government Reform distinguished
between data management and IT management when discussing the roles of chief information
officers and CDOs for the purposes of FEBPA and its amendments to the U.S. Code provisions
that codify the PRA. The committee reported, “Data management, as opposed to IT management
or IT security, is about establishing effective procedures, standards, and controls to ensure quality,
accuracy, transparency, and privacy of data.”42

39 P.L. 104-13, §2; 109 Stat. 166; 44 U.S.C. §3502(7). For the purpose of the PRA, the term information resources is
defined to mean information and related resources such as personnel, funds, and information technology (44 U.S.C.
§3502(6)).
40 Robert Gellman, “Crowdsourcing, Citizen Science, and the Law: Legal Issues Affecting Federal Agencies,”
Woodrow Wilson International Center for Scholars, p. 27, https://www.wilsoncenter.org/sites/default/files/media/
documents/publication/CS_Legal_Barriers_Gellman.pdf#page=28.
41 U.S. Congress, Senate Committee on Governmental Affairs, Paperwork Reduction Act of 1995, report to accompany
S. 244, 104th Cong., 1st sess., S.Rept. 104-8, February 14, 1995, p. 5, https://www.congress.gov/104/crpt/srpt8/CRPT-
104srpt8.pdf#page=8.
42 U.S. Congress, House Committee on Oversight and Government Reform, Foundations for Evidence-Based
(continued...)
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OMB is tasked with developing, coordinating, and overseeing government-wide IRM policies,
principles, standards, and guidelines.43 The role of OMB is discussed later in this report (see
“Implementing Data Standards for Federal Data”). In addition, under specific laws, OMB has a
role in establishing—or working with other agencies to establish—data standards in relation to
specific federal government management issues.44
Chief Data Officers
Title II of FEBPA—the OPEN Government Data Act—established the role of a CDO within each
agency.45 CDOs are responsible for data management.46 The role centralizes data management
within an agency. The House Committee on Oversight and Government Reform believed that the
centralized management of data would improve interoperability and enhance the transparency of
existing federal data.47
Among other functions, an agency’s CDO is responsible for (1) supporting efforts within the
agency to use data for performance improvement;48 (2) ensuring that agency data conform, to the
extent practicable, with data management best practices;49 and (3) standardizing the data formats
of federal datasets.50 CDOs are also responsible for certain agency IRM activities specified in the
PRA.51 This includes improving the utility of agency data to all users within and outside the
agency and the efficient and effective management and use of the data that are collected by the
agency from the public.52
Technical Standards
Technical standards are performance-based or design-based specifications, including the practices
to manage these specifications.53 Technical standards establish, among other criteria, terminology,
rules, and specifications for products and production methods.54 In general, the policy framework
for these standards is the National Technology Transfer and Advancement Act of 1995

Policymaking Act of 2017, report to accompany H.R. 4174, H.Rept. 115-411, 115th Cong., 1st sess., p. 16,
https://www.congress.gov/115/crpt/hrpt411/CRPT-115hrpt411.pdf#page=16.
43 P.L. 104-13, §2; 109 Stat. 167; 44 U.S.C. §3504(a)(1)(A).
44 This includes the DATA Act (P.L. 113-101, §4(a)(1); 128 Stat. 1148) and the Grant Reporting Efficiency and
Agreement Transparency Act (P.L. 116-103, §4; 133 Stat. 3268; 31 U.S.C. §6402(a)(2)), among others.
45 P.L. 115-435, §202(e); 132 Stat. 5541; 44 U.S.C. §3520(a).
46 44 U.S.C. §3520(c)(1).
47 U.S. Congress, House Committee on Oversight and Government Reform, Foundations for Evidence-Based
Policymaking Act of 2017, report to accompany H.R. 4174, H.Rept. 115-411, 115th Cong., 1st sess., p. 16,
https://www.congress.gov/115/crpt/hrpt411/CRPT-115hrpt411.pdf#page=16.
48 44 U.S.C. §3520(c)(8). This section makes a reference to Title 31, Section 1124(a)(2), of the U.S. Code, which
describes the function of an agency’s performance improvement officer, of which the CDO is expected to support with
data.
49 44 U.S.C. §3520(c)(6).
50 44 U.S.C. §3520(c)(3).
51 P.L. 115-435, §202(e); 132 Stat. 5541; 44 U.S.C. §3520(c)(5). This includes responsibility for activities described in
Title 44, Sections 3506(b-d), 3506(f), 3506(i), and 3511, of the U.S. Code.
52 These requirements are described in Title 44, Sections 3506(b)(1)(C) and 3506(c)(1)(A)(vi), of the U.S. Code.
53 P.L. 104-113, §12(d)(4); 110 Stat. 783.
54 OMB, Circular A-119: Federal Participation in the Development and Use of Voluntary Consensus Standards and in
Conformity Assessment Activities, January 27, 2016, p. 15, https://www.whitehouse.gov/wp-content/uploads/2020/07/
revised_circular_a-119_as_of_1_22.pdf.
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(NTTAA).55 OMB provides further instructions to agencies on using technical standards in
Circular A-119, Federal Participation in the Development and Use of Voluntary Consensus
Standards and in Conformity Assessment Activities.
Data standards for federal data have been viewed as being under the umbrella of technical
standards. This may be in part because the use of data standards for federal data largely emerged
along with the federal government’s use of computers in the 1960s and subsequent efforts to use
technical standards for federal IT. OMB has associated data standards with the prevailing policy
on technical standards for federal information processing and dissemination policies, including
interoperability among information systems:
Consistent with existing policies relating to Federal agencies’ use of standards [under
Circular A-119] for information as it is collected or created, agencies must use standards
in order to promote data interoperability and openness.56
Voluntary Consensus Standards
The NTTAA and Circular A-119 prefer technical standards that have been developed by standards
development organizations (SDOs) in the private or nongovernment sectors.57 Statute and policy-
related documents might formally refer to these SDOs as voluntary consensus standards bodies,
which OMB defines as entities that plan, develop, establish, or coordinate voluntary consensus
standards using a specific development process (see the text box “Developing Voluntary
Consensus Standards”).58 Sometimes, statute requires data standards to incorporate standards
developed or maintained by voluntary consensus standards bodies.
NIST is generally involved in coordinating the use by agencies of technical standards developed
by private sector SDOs.59 NIST generally views technical standards as documentary standards,
meaning they are published in documents that detail their requirements.60 NIST’s role in assisting
agencies in using data standards is discussed in “Implementing Data Standards for Federal Data.”
Voluntary consensus standards are intended to:
• eliminate costs to the federal government to develop its own standards,
decreasing procurement costs and the burden to comply with agency regulations;
• incentivize standards for needs that are national in scope, encouraging long-term
growth for U.S. enterprises and promoting efficiency and competition; and

55 P.L. 104-113, §12(a)(3); 110 Stat. 782.
56 OMB, M-13-13, p. 7.
57 OMB, Circular A-119, pp. 17-19.
58 The NTTAA directed all federal agencies to use technical standards developed or adopted by voluntary consensus
standards bodies but did not formally define them (P.L. 104-113, §12(d)(1); 110 Stat. 783). Circular A-119 defines a
voluntary consensus standards body as “a type of association, organization, or technical society that plans, develops,
establishes, or coordinates voluntary consensus standards using a voluntary consensus standards development process”
(p. 16).
59 P.L. 104-113, §12(a)(3); 110 Stat. 782; 15 U.S.C. §272(b)(3). The NTTAA added Section 272(b)(3) to Title 15 of the
U.S. Code. Language in that section was amended by Section 403(2) of P.L. 114-329 (130 Stat. 3023), but the amended
language did not affect the function of NIST to “coordinate the use by Federal agencies of private sector standards,
emphasizing where possible the use of standards developed by private, consensus organizations.”
60 NIST, “Documentary Standards,” https://www.nist.gov/feature-stories/why-you-need-standards/documentary-
standards.
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• further government reliance on the private sector’s expertise to supply cost-
efficient products and services.61
The American National Standards Institute—a private nonprofit organization that the executive
branch has recognized as the coordinator of the U.S. standardization system62—formally accredits
U.S.-based SDOs.63 Standards developed by these accredited organizations are promoted as
demonstrating compliance with the definition of voluntary consensus standard in accordance with
the NTTAA and Circular A-119.64
Developing Voluntary Consensus Standards
Circular A-119 describes voluntary consensus standards bodies as using a development process with certain
attributes:
(i) Openness: The procedures or processes used are open to interested parties. Such parties are provided
meaningful opportunities to participate in standards development on a non-discriminatory basis. The
procedures or processes for participating in standards development and for developing the standard are
transparent.
(i ) Balance: The standards development process should be balanced. Specifically, there should be meaningful
involvement from a broad range of parties, with no single interest dominating the decision-making.
(i i) Due process: Due process shall include documented and publically available policies and procedures,
adequate notice of meetings and standards development, sufficient time to review drafts and prepare views
and objections, access to views and objections of other participants, and a fair and impartial process for
resolving conflicting views.
(iv) Appeals process: An appeals process shall be available for the impartial handling of procedural appeals.
(v) Consensus: Consensus is defined as general agreement, but not necessarily unanimity. During the
development of consensus, comments and objections are considered using fair, impartial, open, and
transparent processes.65
The NTTAA requires federal agencies to participate in the development of voluntary consensus
standards by SDOs when it is in the public’s interest and compatible with an agency’s mission,
authorities, priorities, and resources.66 Circular A-119 provides additional direction to agencies,
including on informing the public of ongoing or planned participation.67 OMB in 2012
emphasized private sector leadership in developing standards, noting that in some circumstances
a federal agency may need to actively engage in standards development to accelerate
technological advances and technology adoption, particularly when a substantial government
investment is being made.68

61 OMB, Circular A-119, p. 14.
62 The White House, United States Government National Standards Strategy for Critical and Emerging Technology,
May 2023, p. 4, https://www.whitehouse.gov/wp-content/uploads/2023/05/US-Gov-National-Standards-Strategy-
2023.pdf.
63 American National Standards Institute, “ANSI Frequently Asked Questions,” https://ansi.org/standards-faqs#ansi.
64 American National Standards Institute, “American National Standards (ANS) Introduction,” https://ansi.org/
american-national-standards/ans-introduction/overview#introduction.
65 OMB, Circular A-119, p. 16.
66 P.L. 104-113, §12(d)(2); 110 Stat. 783.
67 OMB, Circular A-119, p. 29.
68 OMB, M-12-08: Principles for Federal Engagement in Standards Activities to Address National Priorities, January
17, 2012, p. 2, https://www.whitehouse.gov/wp-content/uploads/legacy_drupal_files/omb/memoranda/2012/m-12-
08_1.pdf#page=2.
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OMB specifically prefers agencies to use voluntary consensus standards rather than government-
unique standards.69 Government-unique standards are developed by the federal government
specifically for its use—including in its regulations, procurements, or program areas—and are not
generally used by the private sector unless required by a federal regulation, for federal
procurement reasons, or in connection with participation in a federal program.70
Exceptions to Use
While use of voluntary consensus standards is preferred, it is not mandatory.71 The NTTAA
permits exceptions when it is inconsistent with applicable law or otherwise impractical.72 OMB
states that there is flexibility to allow for agencies to best meet their missions. In addition, there
may be no suitable voluntary consensus standards available for an agency to use. In such
situations, OMB permits an agency to:
• use other suitable standards that “deliver favorable technical and economic
outcomes (such as improved interoperability) and are widely used in the
marketplace;”
• develop its own standards (i.e., government-unique standards);
• use already developed government-unique standards;
• solicit interest from qualified SDOs to develop standards; or
• develop standards using the processes of voluntary consensus standard bodies.73
If an agency elects to use or develop government-unique standards, then it must transmit the
reasons for using such standards to OMB through NIST.74
Types of Data Standards in Federal Laws
Some laws specify certain types of data standards, such as (1) open data standards, (2) data
format standards, (3) data exchange standards, (4) data element standards, and (5) metadata
standards. The terminology for these data standards is not without ambiguity. Some have

69 OMB, Circular A-119, p. 17.
70 OMB, Circular A-119, p. 16.
71 OMB defines use to mean “incorporation of a standard in whole, in part, or by reference for procurement purposes;
inclusion of a standard in whole, in part, or by reference in regulation(s); or inclusion of a standard in whole, in part, or
by reference in other mission-related activities” (Circular A-119, p. 20).
72 P.L. 104-113, §12(d)(3); 110 Stat. 783. OMB defines impractical to include circumstances in which such use would
fail to serve the agency’s regulatory, procurement, or program needs; be infeasible; be inadequate, ineffectual,
inefficient, or inconsistent with the agency mission or the goals of using voluntary consensus standards; be inconsistent
with a provision of law; or impose more burdens or be less useful than the use of another standard (Circular A-119, p.
20).
73 OMB, Circular A-119, pp. 19-20.
74 The NTTAA requires reporting from agencies to OMB on the use of technical standards not developed or adopted by
voluntary consensus standards bodies (P.L. 104-113, §12(d)(3); 110 Stat. 783). Following enactment of the NTTAA,
OMB issued a revised Circular A-119 on February 19, 1998. The circular at the time established that agencies would
submit their reports and reasons for using government-unique standards—defined in the circular at the time as
developed by the government for its own uses—to OMB through NIST (see OMB, “OMB Circular A-119; Federal
Participation in the Development and Use of Voluntary Consensus Standards and in Conformity Assessment
Activities—Final Revision of Circular A-119,” February 19, 1998, 63 Federal Register 8557,
https://www.govinfo.gov/content/pkg/FR-1998-02-19/pdf/98-4177.pdf). The 2016 revision to Circular A-119
maintains this reporting structure among agencies, NIST, and OMB (see pp. 33-34). These reports can be found at
https://www.nist.gov/standardsgov/nttaa-agency-reports.
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characterized it as confusing and unclear and have also observed that different terms could be
used for the same type of data standard.75
In practice, federal data may be standardized in some ways but not necessarily in others. For
example, requiring that data be formatted using XML does not also guarantee that the data’s
potential users will find it equally useful.76 Similarly, some data standard types are not necessarily
mutually exclusive. For example, XML is a data format standard and a data exchange standard; it
is also an open standard. A metadata standard, which could also be called a schema, may define
data elements in ways that are expected by an interpretation of data element standards.
Open Data Standards
Some data standards are open, meaning nonproprietary and available for use without a
dependency on certain technologies or software applications.77 Open may also refer to the ability
to participate in standards development.78 The concept can be interpreted in other ways.79 Many
data standards that are described as open facilitate the use of data in various ways, such as to
establish a common language for data (e.g., definitions, identifiers, and code sets), the exchange
of data (e.g., data formats such as XML), and data catalogs (e.g., online portals to publish
datasets).80 The text box “Examples of Open Data Standards in Federal Policy” notes some of the
ways open data standards appear in federal law.
Examples of Open Data Standards in Federal Policy
Some laws specify the use of open data standards:
•
The Bipartisan Budget Act of 2018 required data standards for federal reporting and state data exchanges
that, to the extent practicable, incorporate “existing nonproprietary standards,” such as XML.81 XML is
considered an “open standard” by its private sector developers, in part because (1) it was developed using an
open process, and (2) it does not require any specific technologies or software to use.82
•
The OPEN Government Data Act defined open government data asset to mean, among other characteristics, a
public dataset in an open format and based on an underlying open standard maintained by a standards
organization.83 In this context, being “based on an underlying open standard” could depend on what open
standard means in a particular context, including any metadata standards that might describe such datasets,
whereas open format could mean an open data format standard, such as XML. Data format standards and
metadata standards are discussed below.

75 Fenareti Lampathaki et al., “Business to Business Interoperability: A Current Review of XML Data Integration
Standards,” Computer Standards and Interfaces, vol. 31 (2009), p. 1047.
76 Lampathaki et al., “Business to Business Interoperability,” p. 1046.
77 Andy Gower, “Open Standards vs. Open Source: A Basic Explanation,” IBM, April 2, 2019, https://www.ibm.com/
blog/open-standards-vs-open-source-explanation/.
78 Open Data Institute, “What Are Open Standards for Data?,” https://standards.theodi.org/introduction/what-are-open-
standards-for-data/. See also the text box in this report “Developing Voluntary Consensus Standards” for a description
of “openness” in this development process.
79 Ken Krechmer, “Open Standards: A Call for Change,” IEEE Communications Magazine, vol 47, no. 5 (May 2009),
p. 89, https://ieeexplore.ieee.org/abstract/document/4939282.
80 Open Data Institute, “What Are Open Standards for Data?”
81 P.L. 115-123, §50606(a); 132 Stat. 230; 42 U.S.C. §711(h)(5)(B)(iii).
82 Erik T. Ray, Learning XML (Sebastopol, CA: O’Reilly and Associates, 2003), p. 8.
83 P.L. 115-435, §202(a)(3); 132 Stat. 5535; 35 U.S.C. §3502(20).
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Data Format Standards
Data format standards may refer to the syntax, encoding, and other often technical specifications
that allow data to be stored, transferred, and then “read” or interpreted by a machine (e.g., a
computer).84 These types of standards include, for example, XML, comma separate values (CSV),
and JavaScript Object Notation (JSON).85 Some types of data formats may be recognizable as file
types, but there are distinctions between data formats and file formats. Given that data format
standards can structure data for transmission, some data format standards are efficient for data
interoperability. The definition of technical standards in OMB’s Circular A-119 includes formats
for the exchange of information.86
Machine-Readable in the Federal Context: Examples of Data Format Standards
•
Data format standards are related to the concept of being machine-readable.87 Federal datasets that can be
made publicly available are expected, among other features, to be machine-readable.88 Machine-readable was
defined by the OPEN Government Data Act to mean “data in a format that can be easily processed by a
computer without human intervention while ensuring no semantic meaning is lost.”89 Agency CDOs are
responsible for standardizing data formats within their respective agencies.90
•
The House Committee on Oversight and Government Reform heard testimony that data format issues
added to the costs of projects using federal data: “Most of the expense of big data projects comes from
extracting information from different sources, transforming those data sets into the same format, and then
loading them into new systems to be analyzed. If Federal data sets were consistently available using machine-
readable formats to begin with, those expensive one-off projects would not be necessary…. When the
government publishes its information, it needs to use non-proprietary data formats, formats that nobody
owns.”91
•
The U.S. Securities and Exchange Commission (SEC) reported that some machine-readable data is required
by 38 out of the 52 statutorily required disclosures it oversees (i.e., forms, schedules, and statements). In
2009, the SEC required certain information to be provided in a machine-readable format and has continued
to require structured data for various information col ections. The SEC reports to Congress semiannually on
public and internal uses of machine-readable data for corporate disclosures, including the costs and benefits
of using machine-readable data.92
Data Exchange Standards
Data exchange standards are generally conceived of as standards for transmitting (sending) and
exchanging (sending and receiving) data across usually disparate systems. XML and JSON are
two commonly used standards for exchanging data, particularly for those exchanges that occur
over the internet.

84 OMB, GSA, and NARA, “Data Standards Concepts and Definitions.”
85 OMB, GSA, and NARA, “Data Standards Concepts and Definitions.”
86 OMB, Circular A-119, p. 15.
87 World Bank, “Open Data Essentials,” https://opendatatoolkit.worldbank.org/en/data/opendatatoolkit/essentials#open-
data.
88 P.L. 115-435, §202(a); 132 Stat. 5535; 44 U.S.C. §3502(20)(a).
89 P.L. 115-435, §202(a); 132 Stat. 5535; 44 U.S.C. §3502(18).
90 P.L. 115-435, §202(e); 132 Stat. 5541; 44 U.S.C. §3520(c)(3).
91 Testimony of Hudson Hollister, in U.S. Congress, House Committee on Oversight and Government Reform,
Legislative Proposals for Fostering Transparency, hearing, 115th Cong., 1st sess., March 23, 2017, Serial No. 115-20,
p. 7, https://www.govinfo.gov/content/pkg/CHRG-115hhrg26499/pdf/CHRG-115hhrg26499.pdf.
92 SEC, Semi-Annual Report to Congress Regarding Public and Internal Use of Machine-Readable Data for Corporate
Disclosures, June 2023, pp. 1-2, https://www.sec.gov/files/2023-fdta-report.pdf#page=5.
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Data exchange standards can facilitate data interoperability. For general IT purposes,
interoperability refers to the ability of data from one system to be used by another system.93 The
text box below discusses one example of how data exchange standards specified in a law have
been implemented. While data exchange and interoperability standards have generally been
identified as a necessary component for sharing data in a generalized way, the specifics of these
standards can vary considerably from case to case. As the text box illustrates, some data exchange
standards establish requirements for data format (e.g., XML), while others may specify content,
structure, and the meaning of data, suggesting a broad range of ways data exchange standards can
present themselves.
Federal Data Exchange Standards: An Example of Implementation
In a section titled, “Data Exchange Standardization for Improved Interoperability,” the Middle Class Tax Relief and
Job Creation Act of 2012 required the Department of Labor to establish data exchange standards for categories of
information required under federal law for the unemployment compensation program.94 The act requires the data
exchange standards—to the extent possible—to be interoperable and nonproprietary, incorporating existing
standards such as XML, and interoperable standards developed and maintained by:
•
an international SDO,
•
intergovernmental partnerships, such as the National Information Exchange Model (NIEM), and
•
federal entities with certain financial-related authorities.95
The department’s final rule designated XML as the data exchange standard, because it fulfil ed many of the law’s
requirements, but noted that interoperable standards developed and maintained by federal entities were not
applicable to unemployment insurance processes.96
The NIEM standards referenced in the act also rely on XML but also establish specifications for what NIEM calls
“information exchange package documentation,” which defines the content, structure, and meaning of an
information exchange message and is considered by NIEM as “the point of interoperability.”97
NIEM standards may be more comprehensive in their design, enabling interoperability in a way that more narrowly
specified data exchange standards cannot. In a joint effort, OMB, GSA, and NARA have referred to NIEM as an
example of a “data standards package” and also a “data standards framework” within their hierarchy of data
standards concepts, as discussed in the earlier text box “Data Standards as a Hierarchy of Concepts.”98
There may be differences in what is intended by interoperability in laws where nonfederal
operations are concerned (e.g., for state-administered federal programs) versus interoperability in
federal interagency operations. The E-Government Act of 2002 defined interoperability to mean
“the ability of different operating and software systems, applications, and services to
communicate and exchange data in an accurate, effective, and consistent manner.”99 The act
applied the term for the purposes of pilot projects that would encourage federal data integration
and data management to reduce the federal collection of duplicate data from the public, facilitate
public access to federal data, and develop software applications that would reduce errors in

93 For example, see the discussion in John Palfrey and Urs Gasser, Interop: The Promise and Perils of Highly
Interconnected Systems (New York: Basic Books, 2012), pp. 5-7.
94 P.L. 112-96, §2104; 126 Stat. 161; 42 U.S.C. §1111. For more on the program, see CRS In Focus IF10336, The
Fundamentals of Unemployment Compensation, by Julie M. Whittaker and Katelin P. Isaacs.
95 42 U.S.C. §1111(a)(3).
96 Department of Labor, Employment and Training Administration, “Federal-State Unemployment Insurance (UI)
Program; Data Exchange Standardization as Required by Section 2104 of the Middle Class Tax Relief and Job Creation
Act of 2012,” 79 Federal Register 9405, February 19, 2014, https://www.govinfo.gov/content/pkg/FR-2014-02-19/pdf/
2014-03496.pdf#page=2. This final rule adopting XML is codified in Title 20, Sections 619.2(a) and 619.3(a), of the
Code of Federal Regulations.
97 NIEM, “IEPDs,” https://niem.github.io/reference/iepd/.
98 OMB, GSA, and NARA, “Data Standards Concepts and Definitions.”
99 P.L. 107-347, §101(a); 116 Stat. 2902; 44 U.S.C. §3601(6).
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information electronically submitted to agencies.100 One of the purposes of the act was to improve
electronic and internet-based public services by improving the effectiveness and efficiency of
interagency electronic processes and integrating related agency functions.101 The act directed
OMB to develop a policy framework for IT standards that included the interoperability standards
required by the PRA.102 While the PRA did not define interoperability, it directed OMB to
promote federal data sharing through interoperability standards.103 One way OMB has
implemented the E-Government Act’s interoperability mandates is through the federal enterprise
architecture (FEA), which is described as a framework and has evolved over time.104 Among
other features and components, the FEA provides a reference model for sharing data across the
federal government and standardizing data exchanges.105 In 2004, GAO testified that “hard work
lies ahead to clarify and evolve the FEA and to ensure that well-managed architecture programs
are actually established and executed, underscore executed, across the Government.”106
Data Element Standards
Numerous statutes reference “data elements.”107 Terms such as variables and atomic data have
also been used for the data element concept.108 The International Organization for Standardization
(ISO)—a large SDO—characterizes data elements as the fundamental units of data that an
organization manages and are necessarily part of the design of databases and of all data
communicated to other organizations:
When an organization needs to transfer data to another organization, data elements are the
fundamental units that make up the message. Messages occur between databases, between

100 P.L. 107-347, §212(d); 116 Stat. 2941.
101 P.L. 107-347, §2(b)(3); 116 Stat. 2901.
102 P.L. 107-347, §101(a); 116 Stat. 2904; 44 U.S.C. §3602(8)(A).
103 P.L. 104-13, §2; 109 Stat. 167; 44 U.S.C. §3504(b)(2).
104 In 2005, OMB issued a congressionally mandated report on its implementation of a section of the E-Government
Act that was concerned with the interoperability of federal systems and data integration. OMB described the FEA as
addressing the goals of that particular section of the act (see OMB, Report to Congress on Implementation of Section
212 of the E-Government Act of 2002, December 17, 2005, p. 3, https://web.archive.org/web/20060124094500/http://
www.whitehouse.gov/omb/egov/documents/Section_212_Report_Final.pdf#page=5). In 2013, OMB issued the
“Federal Enterprise Architecture Framework v2” (see https://obamawhitehouse.archives.gov/sites/default/files/omb/
assets/egov_docs/fea_v2.pdf).
105 The FEA consists of five interrelated reference models, one of which is the data reference model. For more, see
OMB, Federal Enterprise Architecture Framework v2, January 29, 2013, p. 35, https://obamawhitehouse.archives.gov/
sites/default/files/omb/assets/egov_docs/fea_v2.pdf#page=35.
106 Testimony of Randolph Hite in U.S. Congress, House Committee on Government Reform, Subcommittee on
Technology, Information Policy, Intergovernmental Relations and the Census, Federal Enterprise Architecture: A
Blueprint for Improved Federal IT Investment Management and Cross-Agency Collaboration and Information Sharing,
108th Cong., 2nd sess., May 19, 2004, committee print, Serial No. 108-227, p. 21, https://www.govinfo.gov/content/pkg/
CHRG-108hhrg96944/pdf/CHRG-108hhrg96944.pdf#page=25.
107 Examples of the term data element appearing in the U.S. Code include Title 5, Section 552a(o)(C), which concerns
matching of records for the purposes of a “matching agreement” in a matching program; in Title 19, Section
1411(d)(2)(A), which specifies that an interagency steering committee must define, review, and update as necessary a
standard set of data elements for the purposes of the National Customs Automation Program; and in Title 20, Section
1087nn(b), which concerns expected family contributions for the purposes of student financial assistance.
108 As it relates to the term variables, see, for example, Executive Order 13985, Section 9: “Many Federal datasets are
not disaggregated by race, ethnicity, gender, disability, income, veteran status, or other key demographic variables.
This lack of data has cascading effects and impedes efforts to measure and advance equity” (available at
https://www.govinfo.gov/content/pkg/FR-2021-01-25/pdf/2021-01753.pdf). As it relates to the term atomic data, see,
for example, Center for Government Excellence at Johns Hopkins University, “OD [Open Data] Standards Definition,”
https://datastandards.directory/glossary#glossary-definition.
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databases and humans, and between humans. Moreover, the structure of databases don’t
have to be the same across organizations. So, the common unit for transferring data and
related information is the data element.109
Data element standards establish how to represent data, especially when there is an opportunity
for confusion.110 These standards may, for example, establish definitions that should be
consistently applied, constrain the data type to integer or date, require dollars and cents ($4.60)
versus whole dollars ($5), or require only FIPS codes for geographic entities. As such, data
element standards are comparable with the definition of technical standards in OMB’s Circular
A-119 by establishing common and repeated rules for the production of data and by establishing
definitions and other specifications.111
Examples of Data Element Standards in Federal Policy and Their Implementation
•
The Personal Responsibility and Work Opportunity Reconciliation Act of 1996 required the Department of
Health and Human Services (HHS) to define data elements for certain reports that states must submit on the
Temporary Assistance for Needy Family (TANF) program.112 The Administration for Children and Families
(ACF), which administers TANF, noted that the information must be comparable and reliable in order to
comply with the law’s requirements, including reporting to Congress, and that “unless the reported data
meet certain standards, [ACF] cannot adequately meet [its] responsibilities under the law.”113 In effect, ACF
promulgated standard data definitions that correspond to program-specific terms, such as family.114
•
The Grant Reporting Efficiency and Agreements Act of 2019 requires data standards for federal grants and
other financial awards, including definitions for data elements and standards that render reported information
machine-readable.115 A GAO report indicates that definitions for 540 data elements related to government-
wide awardmaking have been developed but are not ful y consistent with the act’s statutory requirements.116
Specifically, additional work is needed to specify the formatting requirements that allow the data to be
machine-readable and consistently processed (e.g., California could be reported as “California,” “Cali.,” “CA,”
or “Ca”). GAO also found that certain data element definitions were not consistent with several “leading
practices” for developing data definitions, including some that were ambiguous, which may increase
uncertainty among federal award recipients when reporting data and result in inconsistent and
noncomparable federal data.117
Semantic Standards
Sometimes, the term semantic standards is used to ensure that data elements are consistently and
accurately interpreted by humans and computers.118 Some have characterized the term as being

109 ISO, “Information Technology—Metadata Registries (MDR)—Part I: Framework,” ISO/IEC 11179-1:2023,
January 2023, p. 15. ISO standards are copyright protected.
110 Center for Government Excellence, “OD [Open Data] Standards Definition.”
111 OMB, Circular A-119, p. 15.
112 P.L. 104-193, §411(a)(6); 110 Stat. 2150; 42 U.S.C. §611(7).
113 ACF, “Temporary Assistance for Needy Families Program (TANF),” 64 Federal Register 17858-17859, April 12,
1999, https://www.govinfo.gov/content/pkg/FR-1999-04-12/pdf/99-8000.pdf#page=139.
114 45 C.F.R. §265.2.
115 P.L. 116-103, §4; 133 Stat. 3268; 31 U.S.C. §6402(a)(3)(A); 31 U.S.C §6402(c)(1).
116 See the “Highlights” section of GAO, Grants Management: Action Needed to Ensure Consistency and Usefulness of
New Data Standards.
117 GAO, Grants Management: Action Needed to Ensure Consistency and Usefulness of New Data Standards, pp. 19-
20, 24-26.
118 Panos Alexopoulos, Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas (Sebastopol, CA:
O’Reilly Media, 2020), p. 4.
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weakly defined while also noting some overlap with the concept of metadata,119 which is
discussed separately below. Additionally, organizations that develop what could be considered
semantic standards do not necessarily identify their standards as such.120 Nevertheless, in general,
the function of semantic standards is to provide a language for data that is relevant for the domain
in which the data are used.121 While some types of data standards, such as XML, can usually be
implemented without a specific dependency on domain, semantic data standards, in contrast,
represent not only “a way of looking at the world” but an agreement among groups about what
the world looks like.122
Semantic data standards have been characterized as enabling data interoperability and automating
processes and transactions among data users.123 Some observers note that interoperability
standards have largely enabled technical aspects, with less attention paid to the semantic
aspects.124 Moreover, while semantic standards have been characterized as critical for enabling
“true” interoperability,125 it may be difficult for users to search for, identify, and reuse what is
available.126
While some laws refer to the semantic meaning of data, this is uncommon.127 Some argue that
unless semantic standards and specifications are identified, aligned, documented, managed, and
promoted for reuse, the result is wasted investments in IT, which is more acute for governments
given the size of their IT investments. This argument follows from the observation that efforts by
other countries to use semantic standards for government operations and public administration
have not produced widely accepted agreements on semantics for fundamental concepts.128
Metadata and Metadata Standards
In practice, metadata is often simplified to mean “data about data”—for example, a column
heading in a spreadsheet that describes the contents in the cells underneath the heading. The ISO
explains that the usefulness of data for sharing is dependent on its meaning, type, format, and
structure—all of which are metadata—being known to data users.129

119 Vassilios Peristeras, “Semantic Standards: Preventing Waste in the Information Industry,” IEEE Intelligent Systems,
vol. 28, no. 4 (July/August 2013), pp. 72-73.
120 Peristeras, “Semantic Standards,” p. 73.
121 Boris Otto, Erwin Folmer, and Verena Ebner, “A Characteristics Framework for Semantic Information Systems
Standards,” Information Systems and e-Business Management, vol. 10, no. 4 (December 2012), p. 576.
122 Peristeras, “Semantic Standards,” p. 72.
123 M. Lynne Markus, Charles W. Steinfeld, and Rolf T. Wigand, “Industry-Wide Information Systems Standardization
as Collective Action: The Case of the U.S. Residential Mortgage Industry,” MIS Quarterly, vol. 30 (August 2006), p.
453.
124 Lampathaki et al., “Business to Business Interoperability,” p.1046. See also Peristeras, “Semantic Standards,” p. 72.
125 Alexopoulos, Semantic Modeling for Data, p. 8. See also Erwin Folmer, Paul Oude Luttighuis, and Jos van
Hillegersberg, “Do Semantic Standards Lack Quality: A Survey Among 34 Semantic Standards,” Electronic Markets,
vol. 21 (June 2011), p. 100; and Lampathaki et al., “Business to Business Interoperability,” p. 1046.
126 Peristeras, “Semantic Standards,” p. 73. A similar point is made in Lampathaki et al., “Business to Business
Interoperability,” p. 1046, which characterizes the diversity of standards as a dilemma that makes achieving “true”
interoperability more challenging.
127 The idea of “semantic meaning” is included in the statutory definition of machine-readable that was discussed in the
earlier section on “Data Format Standards” and is also used in the Financial Data Transparency Act of 2022 (P.L. 117-
263, §5811(a); 136 Stat. 3422; 12 U.S.C. §5334(c)(1)(B)(ii)).
128 Peristeras, “Semantic Standards,” pp. 72-73.
129 ISO, “Information Technology—Metadata Registries (MDR)—Part I: Framework,”, p. 28. ISO standards are
copyright protected.
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Metadata standards may establish specific rules for creating and managing metadata. In the same
way there is variation in data standards, there are differences in metadata standards. Metadata
standards may prescribe how to define data elements.130 Some metadata standards are designed
for publishing data on the web, including for producing machine-readable metadata.131 Metadata
standards may provide “controlled vocabularies,” which control the actual values the metadata
may take, and “content standards,” such as what specific metadata to record.132 Metadata
standards may contribute to the quality of the underlying data.133
Examples of Metadata in the Federal Context
•
The OPEN Government Data Act defined metadata as “structural or descriptive information about data such
as content, format, source, rights, accuracy, provenance, frequency, periodicity, granularity, publisher or
responsible party, contact information, method of col ection, and other descriptions.”134 Some subsequent
laws use the same definition.135 The Geospatial Data Act of 2018 defined metadata for geospatial data.136
•
OMB advises agencies to “col ect or create information in a way that supports downstream interoperability
among information systems and streamlines dissemination to the public, where appropriate, by creating or
col ecting all new information electronically by default, in machine-readable open formats, using relevant data
standards, that upon creation includes standard extensible metadata in accordance with OMB guidance.”137
•
A Senate committee recently heard testimony that AI can create links among datasets by using metadata in
ways that may have been more difficult in the past, which may improve how the federal government delivers
services.138
Implementing Data Standards for Federal Data
Congress has sometimes directed specific agencies to play a government-wide role in data
standards and guide and oversee their use in the executive branch. This section discusses the ways

130 See GAO, DATA Act: Data Standards Established, but More Complete and Timely Guidance Is Needed to Ensure
Effective Implementation, GAO-16-261, January 2016, pp. 11-12, https://www.gao.gov/assets/gao-16-261.pdf#page=
16. In this report, GAO evaluated well-constructed data definitions, pointing to “leading practices” from ISO/IEC
11179-4—which is a standard concerned with creating and managing metadata—and evaluated the extent to which
OMB and the Treasury Department developed definitions for financial reporting that were consistent with those leading
practices.
131 Annette Griener et al., “Data on the Web Best Practices,” entry for Metadata, World Wide Web Consortium,
January 31, 2017, https://www.w3.org/TR/dwbp/#metadata. In addition to discussing standardized metadata terms,
these best practices also discuss standardized vocabularies (see entry for Data Vocabularies at the site listed above).
132 Jenn Riley, Understanding Metadata: What Is Metadata, and What Is It For? (Baltimore, MD: National Information
Standards Organization, 2017), pp. 17-18, https://groups.niso.org/higherlogic/ws/public/download/17446/
Understanding%20Metadata.pdf#page=20.
133 Bruce Bargmeyer and Daniel Gillman, “Metadata Standards and Metadata Registries: An Overview,” U.S. Bureau
of Labor Statistics, 2000, p. 9, https://www.bls.gov/osmr/research-papers/2000/pdf/st000010.pdf#page=9.
134 P.L. 115-435, §202(a); 132 Stat. 5535; 44 U.S.C. §3502(19).
135 See, for example, P.L. 117-263, §5811(a); 136 Stat. 3422; 12 U.S.C. §5334(a)(2).
136 P.L. 115-254, §752; 132 Stat. 3415; 43 U.S.C. §2801(11). Metadata for geospatial data means “information about
geospatial data, including the content, source, vintage, accuracy, condition, projection, method of collection, and other
characteristics or descriptions of the geospatial data.”
137 OMB, “Revision of OMB Circular A-130, ‘Managing Information as a Strategic Resource,’” 81 Federal Register
49689, July 28, 2016, p. 15, https://www.whitehouse.gov/wp-content/uploads/legacy_drupal_files/omb/circulars/A130/
a130revised.pdf.
138 Testimony of Beth Blauer, in U.S. Congress, Senate Committee on Homeland Security and Governmental Affairs,
Harnessing AI to Improve Government Services and Customer Services, hearing, 118th Cong., 2nd sess., January 10,
2024.
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OMB and NIST provide guidance to agencies on using data standards and the evolution of their
roles.
Office of Management and Budget
OMB’s role in providing guidance to agencies on data standards for federal data has evolved as
the federal government has sought to capitalize on IT. For example, in September 1967, OMB
(then known as the Bureau of the Budget) issued government-wide policies for standardizing data
elements, in part to increase the federal government’s ability to leverage automatic data
processing—the term that was largely replaced by information technology.139 OMB
acknowledged at the time that computers and other technology were expanding opportunities for
data integration, data aggregation, and data exchange but that the value of this data use could not
be realized unless there were uniform understandings of data and the development and
application of data standards.140
As discussed below, OMB situates data standards in its guidance on information management, in
statistical policy directives, and in a memorandum related to a federal data strategy. While OMB’s
guidance on IRM and federal statistical policy stems from its authority under the PRA and other
statutes, the federal data strategy was initially a component of the Trump Administration’s
President’s Management Agenda, which established a cross-agency priority goal to leverage data
as a strategic asset.141
Data Standards for Information Resource Management
OMB issues guidance on “standards,” as a generic term, to implement IRM policies.142 A Senate
committee report stated that IRM in the PRA was envisioned to include “IRM policy, IRM
utilization to minimize paperwork burden, information dissemination, statistics, records
management, information security and privacy, and IT management.”143 Among other IRM
functions, the administrator of the Office of Information and Regulatory Affairs—as delegated by
the director of OMB—is tasked with:

139 The legislative history indicates that the meaning of automatic data processing has evolved to mean IT for federal
purposes (P.L. 104-106, §5602(b); 110 Stat. 699).
140 Executive Office of the President, Bureau of the Budget, Circular No. A-86. Standardization of Data Elements and
Codes in Data Systems, September 30, 1967, in Department of Commerce, National Bureau of Standards, Federal
Information Processing Standards Index, January 1, 1971, p. 35, https://files.eric.ed.gov/fulltext/ED048904.pdf#page=
37.
141 Executive Office of the President, President’s Management Agenda, March 20, 2018, pp. 17-19,
https://trumpadministration.archives.performance.gov/PMA/Presidents_Management_Agenda.pdf#page=17. The
GPRA Modernization Act of 2010 requires OMB to develop “federal government priority goals” (P.L. 111-352, §5;
124 Stat. 3873; 31 U.S.C. §1120(a)(1)). In practice, these are called cross-agency priority goals. GAO states that each
Administration typically releases a President’s management agenda that communicates and organizes these goals and
implementation strategies (see GAO, Government Performance Management: Actions Needed to Improve
Transparency of Cross-Agency Priority Goals¸ GAO-23-106354, April 2023, p. 1, https://www.gao.gov/assets/
d23106354.pdf#page=5). For background, see CRS Report R42379, Changes to the Government Performance and
Results Act (GPRA): Overview of the New Framework of Products and Processes, by Clinton T. Brass.
142 P.L. 104-13, §2; 109 Stat. 167; 44 U.S.C. §3504(b)(1).
143 U.S. Congress, Senate Committee on Governmental Affairs, Paperwork Reduction Act of 1995, report to accompany
S. 244, 104th Cong., 1st sess., S.Rept. 104-8, February 14, 1995, p. 13, https://www.congress.gov/104/crpt/srpt8/CRPT-
104srpt8.pdf#page=16.
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1. developing and overseeing uniform IRM policies, principles, standards, and
guidelines,144 and
2. developing and using common standards for information collection, storage,
processing and communication to foster greater federal data sharing, including
standards for security, interconnectivity, and interoperability.145
OMB issues guidance to agencies that specifically includes ways that data standards could be
used for IRM.146 For example, in Circular A-130, OMB advises agencies on using data standards
to manage information, including:
• using open data standards to the maximum extent possible when implementing IT
systems,
• standards that enable data governance, and
• using data standards to support downstream interoperability among information
systems and to streamline dissemination of information to the public.147
Data Standards for Federal Statistics
OMB oversees federal statistical data management through statistical policy directives.148 Some
of these directives establish specific definitions and other specifications for federal data and
operate as data standards in this way. There is no single approach to how the data standards under
these directives are developed. While largely for use by federal statistical agencies, in some cases
these data standards are used for other nonstatistical purposes, including nonfederal ones, and to
manage certain data, such as that collected from administrative forms.
For example, one of OMB’s statistical directives establishes the North American Industry
Classification System (NAICS) for classifying business establishments by their type of economic
activity and is to ensure that business establishment data across the federal statistical system are

144 44 U.S.C. §3504(a)(1)(A). The PRA specifically requires the director to delegate to the administrator the authority
to administer the functions that are contained within the act (44 U.S.C. §3503(b)).
145 44 U.S.C. §3504(b)(2). The PRA does not define interoperability for its purposes, although a committee report on
the PRA makes several references to interoperability (U.S. Congress, Senate Committee on Governmental Affairs,
Paperwork Reduction Act of 1995, report to accompany S. 244, 104th Cong., 1st sess., S.Rept. 104-8, February 14,
1995, https://www.congress.gov/104/crpt/srpt8/CRPT-104srpt8.pdf). For example, (1) “the bill maximizes utility by
placing an emphasis on interoperability of agency systems and improvements in data sharing. These steps are meant to
capitalize on the advantages that information technologies offer for streamlining agency operations, enhancing public
access to government information, and reducing burdens on the public” (p. 23); (2) “an additional new purpose of the
bill is to strengthen the partnership between the Federal government and State, local and tribal governments by
minimizing information collection burdens and maximizing the utility of information collected by Federal agencies.
This will require additional attention be paid to establishing common standards for data exchange and for
interoperability among systems” (p. 24); and (3) “Developing interoperability among statistical systems in the different
agencies also is important for improving access to valid and current data. To facilitate this coordination, the bill
requires OMB to establish an interagency council, headed by the Chief Statistician and consisting of the heads of the
major statistical agencies and representatives of other statistical agencies under rotating membership” (p. 27).
146 For example, OMB issued guidance to agencies on metadata standards in conjunction with Executive Order 13642,
describing it as helping “institutionalize the principles of effective information management at each stage of the
information’s life cycle to promote interoperability and openness. Whether or not particular information can be made
public, agencies can apply this framework to all information resources to promote efficiency and produce value”
(OMB, M-13-13, p. 1).
147 OMB, Circular A-130, pp. 8 and 15.
148 44 U.S.C. §3504(e)(3). See also CRS Insight IN12197, The Federal Statistical System: A Primer, by Taylor R.
Knoedl.
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comparable and can be aggregated for analysis.149 NAICS is jointly developed by Mexico’s
Instituto Nacional de Estadistica y Geografia (National Institute of Statistics and Geography),
Statistics Canada, and OMB through the interagency Economic Classification Policy Committee.
The Bureau of Labor Statistics uses NAICS for the Current Employment Statistics program,
which is used to generate the monthly Employment Situation Summary (known more commonly
as “the monthly jobs report”).150 For nonstatistical purposes, the Small Business Administration
uses NAICS to determine what is a “small business.”151
Another statistical policy directive establishes the Standard Occupational Classification (SOC) for
categorizing all occupations in the U.S. national economy for which work is performed for pay or
profit in the public, private, and military sectors.152 SOC is developed by a federal interagency
technical working group. OMB states that SOC promotes a common language and encourages
state and local governments to adopt SOC for classifying and analyzing occupations. At least one
state requires SOC codes for employers’ quarterly unemployment insurance reports.153
Data Standards and a Federal Data Strategy
In 2019, OMB developed a federal data strategy and issued guidance to agencies on data
management activities in federal programs and statistical programs and in support of agency
missions.154 The strategy was described as “a framework of operational principles and best
practices that help agencies deliver on the promise of data in the 21st century.” The strategy has
also been characterized as a vision for achieving a data-driven federal government by 2030.155
Among other practices, the strategy called for adopting, adapting, creating as needed, and
implementing data standards within relevant communities of interest in order to maximize data
quality and facilitate data use, access, sharing, and interoperability.156
OMB directed agencies to adhere to requirements within government-wide action plans it would
develop.157 Action plans were developed for 2020 under the Trump Administration158 and 2021
under the Biden Administration,159 with each establishing specific milestones for agencies to meet
and timelines for doing so. OMB said it would assess agency progress through its existing
oversight and coordination mechanisms, such as those stemming from the budget development

149 OMB, “North American Industry Classification System—Revision for 2022; Update of Statistical Policy Directive
No. 8, North American Industry Classification System: Classification of Establishments; and Elimination of Statistical
Policy Directive No. 9, Standard Industrial Classification of Enterprises,” December 21, 2021, 86 Federal Register
72278, https://www.govinfo.gov/content/pkg/FR-2021-12-21/pdf/2021-27536.pdf#page=2. In practice, this may be
referred to more simply as Statistical Policy Directive No. 8.
150 See Department of Labor, Bureau of Labor Statistics, “Employment Situation Technical Note,”
https://www.bls.gov/news.release/empsit.tn.htm.
151 13 C.F.R. §121.101.
152 OMB, “Standard Occupational Classification (SOC) System—Revision for 2018,” November 28, 2017, 82 Federal
Register 56272, https://www.govinfo.gov/content/pkg/FR-2017-11-28/pdf/2017-25622.pdf#page=2. In practice, this
may be referred to more simply as Statistical Policy Directive No. 10.
153 Louisiana uses SOC codes in this way. See La. R.S. 23:1660(A)(2).
154 OMB, M-19-18.
155 Executive Office of the President, Federal Data Strategy: 2021 Action Plan, October 2021, pp. 1-2,
https://strategy.data.gov/assets/docs/2021-Federal-Data-Strategy-Action-Plan.pdf#page=4.
156 OMB, M-19-18, p. 5.
157 OMB, M-19-18, p. 7.
158 Executive Office of the President, Federal Data Strategy: 2020 Action Plan, December 2019,
https://strategy.data.gov/assets/docs/2020-federal-data-strategy-action-plan.pdf.
159 Executive Office of the President, Federal Data Strategy: 2021 Action Plan.
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process, information collections under the PRA, and system of records notices under the Privacy
Act of 1974.160 The 2021 action plan was released in October of that year. It noted that it was a
transition year for executive branch agencies under a new Administration with its own priorities,
and it indicated that agencies had until the end of the calendar year to work on the plan’s
“aspirational milestones.” In 2023, a report from a science and technology policy group suggested
that the federal data strategy needs additional effort by OMB to implement.161
National Institute of Standards and Technology
Both executive and congressional actions have shaped the role of NIST in data standards for
federal data. By executive order, President Richard Nixon transferred to the National Bureau of
Standards (NBS, NIST’s precursor) functions that had previously been performed by OMB as
federal agencies began to use computers more widely.162 The transfer resulted in NIST assuming a
direct role in developing guidance to federal agencies on standardizing data elements, using data
dictionaries, and managing data.163 For example, NBS identified types of data standards,
established policies for different types of data standards, and defined various agency
responsibilities for implementing data standards.164
NIST has a statutory role in supporting agencies in using voluntary consensus standards, which
were discussed earlier. It also issues guidance to agencies that is broadly concerned with federal
IT management. This guidance can, at times, have implications for agencies’ use of data
standards.
Information Technology Guidance
The Computer Security Act of 1987 charged NIST with “developing standards, guidelines, and
associated methods and techniques for computer systems.”165 The act was largely concerned with
ensuring standards for government-wide computer security and the privacy of sensitive
information in federal computer systems.166 In this way, the act provided a framework for
managing risks that accompany the federal use of IT.

160 OMB, M-19-18, p. 7.
161 Eric Egan, “Reviving and Reimagining the Federal Data Strategy for Mission Success,” Information Technology
and Innovation Foundation, June 5, 2023, https://itif.org/publications/2023/06/05/reviving-and-reimagining-the-federal-
data-strategy-for-mission-success/.
162 Executive Order 11717, “Transferring Certain Functions from the Office of Management and Budget to the General
Services Administration and the Department of Commerce,” 38 Federal Register 12315, May 9, 1973. NBS was
renamed as NIST by the Omnibus Trade and Competitiveness Act of 1988 (P.L. 100-418, §5101; 102 Stat. 1427).
163 For examples of some of this guidance, see NBS, Guideline for Choosing a Data Management Approach, December
11, 1984, https://nvlpubs.nist.gov/nistpubs/Legacy/FIPS/fipspub110.pdf; and Judith J. Newton, Guide on Data Entity
Naming Conventions, October 1987, https://www.govinfo.gov/content/pkg/GOVPUB-C13-
94ab71a32c5fe6f2c61a6c3ba14c307a/pdf/GOVPUB-C13-94ab71a32c5fe6f2c61a6c3ba14c307a.pdf.
164 Department of Commerce, “Standards of Data Elements and Representations,” 38 Federal Register 33484,
December 5, 1973, https://www.govinfo.gov/content/pkg/FR-1973-12-05/pdf/FR-1973-12-05.pdf#page=38. See also
NBS, Standardization of Data Elements and Representations, December 5, 1973, https://www.govinfo.gov/content/
pkg/GOVPUB-C13-014d6f2898d47a6721672caa87ff91e4/pdf/GOVPUB-C13-
014d6f2898d47a6721672caa87ff91e4.pdf#page=6.
165 P.L. 100-235, §3(2); 101 Stat. 1725.
166 Section 2(b) of P.L. 100-235 enumerates the specific purposes of the act, including (1) “assign to the National
Bureau of Standards responsibility for developing standards and guidelines for Federal computer systems, including
responsibility for developing standards and guidelines needed to assure the cost-effective security and privacy of
sensitive information in Federal computer systems;” (2) “to provide for promulgation of such standards and
(continued...)
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Subsequent to the act’s passage, the Department of Commerce—in which NIST operates—
withdrew NIST’s guidance to agencies that concerned certain data standards. A Federal Register
notice of the withdrawal stated that such data standards were critical to federal information
processing systems at the time and required special emphasis to support cost-effective
information processing.167 The department stated that the data standards were obsolete, as the
prevailing policy emphasis was now on a broader view of IT management.
NIST’s role in the federal use of standards took further shape when the NTTAA was enacted. As
discussed earlier, the NTTAA established a preference for agencies to use voluntary consensus
standards developed in the private sector. The NTTAA requires NIST to coordinate and promote
agencies’ use of private sector standards.168 Thus, when a law specifies for data standards to
incorporate voluntary consensus standards, NIST may be best positioned to support agencies in
implementing this kind of requirement.
In accordance with certain federal IT laws, NIST develops guidance to agencies for the efficient
operation of federal computer systems and for the security and privacy of such systems.169 This
guidance implicates federal data management and data standards in some cases, such as the
categorizing of data collected or maintained by or on behalf of a federal agency for information
security purposes.170 NIST also publishes various reports, including the “special publications”
series that provides guidelines, technical specifications, and recommendations to agencies.171
These publications may inform, for example, how an agency might use metadata to manage
data172 or data governance and standards to enable “big data” interoperability.173 Additionally,
NIST produces reliable standardized technical and scientific data—formally defined as standard
reference data in statute—which it may copyright and sell to scientists, engineers, and the
public.174

guidelines;” (3) “to require establishment of security plans by all operators of Federal computer systems that contain
sensitive information;” and (4) “to require mandatory periodic training for all persons involved in management, use, or
operation of Federal computer systems that contain sensitive information.”
167 Department of Commerce, “Standardization of Data Elements and Representations,” 57 Federal Register 30116,
July 8, 1992, https://www.govinfo.gov/content/pkg/FR-1992-07-08/pdf/FR-1992-07-08.pdf#page=26.
168 P.L. 100-235, §12(a)(3); 110 Stat. 782.
169 For example, NIST will cite its responsibilities under P.L. 104-106, §5131 (110 Stat. 687); see also 41 U.S.C.
§1441(a)(1); P.L. 107-347, §§302-303 (116 Stat. 2957); 15 U.S.C. §278g-3(a)(2-3); and 40 U.S.C. §11331(a-b). See
also NIST, “Compliance FAQs: Federal Information Processing Standards (FIPS),” https://www.nist.gov/standardsgov/
compliance-faqs-federal-information-processing-standards-fips.
170 For these standards, see NIST, Standards for Security Categorization of Federal Information and Information
Systems, February 2004, p. 1, https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.199.pdf#page=5.
171 See NIST, “Publications,” https://csrc.nist.gov/publications.
172 NIST has described metadata as enhancing control over information flow and supporting the enforcement of
allowable information flows. NIST explains that “information flow control” regulates where information can travel
within and between systems, in contrast to who can access the information, and without regard to subsequent access to
that information. NIST, Security and Privacy Controls for Information Systems and Organizations, September 2020,
pp. 28-30, https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53r5.pdf#page=55.
173 For example, NIST described key topics in the “NIST Big Data Interoperability Framework” (NBDIF). The NBDIF
describes what is needed to leverage and draw insights from “big data,” a term used to characterize the large amounts
of data that are available in a “networked, digitized, sensor-laden, and information-driven world.” One volume of the
NBDIF discusses “big data management” (SP-1500-1r2). Another volume of the NBDIF discusses technical standards
for big data and gaps in those standards (SP-1500-7r2). Access to each NBDIF volume can be found at
https://www.nist.gov/itl/big-data-nist/big-data-nist-documents/nbdif-version-30-final.
174 This function is authorized by the Standard Reference Data Act (P.L. 90-396), as amended (see P.L. 114-329, §108;
130 Stat. 2987; 15 U.S.C. §§290-294f). The definition of standard reference data is “(A) either (i) quantitative
(continued...)
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Recent Developments
The William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021
tasked NIST with developing best practices for using datasets to train AI systems.175 These best
practices are to address the use of metadata standards that describe the data’s origins (i.e.,
provenance), the intent behind their creation, their permissible uses, their descriptive
characteristics (including populations that are included and excluded), and any other properties
determined by NIST.
In July 2021, NIST announced a project to focus on data management and data protection in
certain use cases.176 The project is intended to apply a “zero trust” security approach and is
supposed to result in recommendations for defining data classifications and rules and
standardizing how to communicate such classifications and rules at scale within the financial,
government, manufacturing, technology, and health care sectors. The project intends to leverage
existing NIST guidance and standards, including a metadata schema to describe attributes of
subjects that might attempt to access otherwise protected data.177
Considerations for Congress
Data standards may contribute to the usability of federal data for a particular purpose, including
their readiness for use and their quality. Data standards have some clear applications in federal
data management, particularly for agency operations and program management. The
interoperability of federal data may also depend on data standards in various ways. Congress may
also benefit from the use of data standards for federal data to assist in its decisionmaking.
Lawmakers may continue to identify a role for data standards in achieving certain policy
outcomes in future legislation and for specific programs, regulatory matters, and government-
wide operations. When Congress seeks to require data standards for federal data, it may consider
addressing (1) such standards within specific policies, (2) such standards government-wide, and
(3) the specification of data standards in the law and in cases of federal data interoperability.

information related to a measurable physical, or chemical, or biological property of a substance or system of substances
of known composition and structure; (ii) measurable characteristics of a physical artifact or artifacts; (iii) engineering
properties or performance characteristics of a system; or (iv) one or more digital data objects that serve—(I) to calibrate
or characterize the performance of a detection or measurement system; or (II) to interpolate or extrapolate, or both, data
described in subparagraph (A) through (C) [as in original]; and (B) that is critically evaluated as to its reliability under
15 U.S.C. §290b (15 U.S.C. §290a(1)).” See also NIST, “Standard Reference Data,” https://www.nist.gov/srd.
175 P.L. 116-283, §5301; 134 Stat. 4538; 15 U.S.C. §278h-1(f)(1).
176 Karen Scarfone and Murugiah Souppaya, Data Classification Practices: Facilitating Data-Centric Security
Management, NIST, p. 3, https://www.nccoe.nist.gov/sites/default/files/legacy-files/data-classification-project-
description-final.pdf. NIST describes data-centric security management as aiming “to enhance the protection of
information (data) regardless of where the data resides or who it is shared with. Data-centric security management
necessarily depends on organizations knowing what data they have, what its characteristics are, and what security and
privacy requirements it needs to meet so the necessary protections can be achieved.” Data-centric security management
is part of “zero trust,” which NIST describes as a “cybersecurity paradigm” and means “a collection of concepts and
ideas designed to minimize uncertainty in enforcing accurate, least privilege per-request access decisions in
information systems and services in the face of a network viewed as comprised” (see Scott Rose et al., Zero Trust
Architecture, NIST, August 2020, p. 4, https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-
207.pdf#page=13).
177 Rose et al., Zero Trust Architecture, p. 8. Specifically, it cites Paul A. Grassi et al., Attribute Metadata: A Proposed
Schema for Evaluating Federated Attributes, NIST, January 2018, p. 1, https://nvlpubs.nist.gov/nistpubs/ir/2018/
NIST.IR.8112.pdf#page=11.
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Policy Options for Data Governance and Data Management
Congress may consider ensuring that data standards within a given policy are accompanied by
formal mechanisms for data management. For example, legislation could require data governance
functions, such as developing the policies and processes for using data standards, establishing
leadership and accountability roles, and ensuring oversight. Congress has identified a role for
GAO in reporting on certain government-wide data standards. Likewise, lawmakers could
consider specifying a role for inspectors general in auditing data standards management within
agencies. When considering leadership and accountability for data standards, Congress may
specifically consider CDOs.
Designating a Role for Chief Data Officers
Lawmakers may want to designate a role for an agency’s CDO in implementing data standards,
including in policies that concern only a specific program or activity. Including the CDO may
contribute to how such standards are used and how they cohere with agency-wide data
governance and IRM policies.178 Including the CDO may also affect implementation of data
standards as they are specified by Congress in a law. The Federal CDO Council identified agency
CDOs as having a role in developing a common language for data-related terms within an agency,
including standards-related terminology.179 Involving the CDO may mitigate inconsistent
interpretations of concepts and terms, particularly if such terms do not have single definitive
interpretations in practice.
Although Congress intended CDOs to centralize responsibility for federal data management, there
may be practical constraints on a CDO’s ability to oversee data standards within an agency. CDOs
are tasked with performing many functions.180 In 2023, a majority of CDOs in medium and small
agencies did not identify the CDO role as their primary responsibility, meaning that these CDOs
also have other official responsibilities within their agencies,181 and a majority of CDOs in these
agencies also do not have full-time equivalents to support their CDO functions.182 Thus, when
requiring data standards, Congress may consider providing funding to support data standards
management specifically—and perhaps data management more generally—within agencies.
Government-Wide Policy Options for Managing Data Standards
Federal data management practices will likely continue to develop, which may have a bearing on
federal data standards. Part of this development may coincide with the implementation of laws
such as FEBPA, while some may occur as agencies operationalize their data strategies. OMB
acknowledges data governance in reducing barriers to using AI, suggesting that agencies may
need to emphasize how they manage their data.183 Similarly, NIST acknowledges the role of data

178 Consistent with Title 44, Section 3506(b), of the U.S. Code, OMB’s Circular A-130 directs agencies to have
agency-wide data governance policies that “clearly establish the roles, responsibilities, and processes by which agency
personnel manage information as an asset and the relationships among technology, data, agency programs, strategies,
legal and regulatory requirements, and business objectives” (p. 9).
179 Federal CDO Council, CDO Playbook: Advancing the Federal Data Strategy, 2021, p. 8, https://resources.data.gov/
assets/documents/CDO_Playbook_2021.pdf#page=8.
180 44 U.S.C. §3520(c).
181 Federal CDO Council, CDO Council—Summer 2023 Survey, December 2023, p. 17, https://www.cdo.gov/assets/
documents/cdoc_final_10_26_2023.pdf#page=17.Error! Hyperlink reference not valid.
182 Federal CDO Council, CDO Council—Summer 2023 Survey, p. 66.
183 OMB, M-24-10, p. 11.
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standards in AI and in “big data” and interoperability, also suggesting that data management will
play a role in these emerging areas.184 As these activities move forward, Congress may consider
reconciling the relevant policy frameworks to specifically account for federal data standards
management and consider the role of NIST in coordinating the federal use of data standards.
Policy Coordination
Data standards operate within multiple policy frameworks, including those governing technical
standards and information resources. These policy frameworks may need further coordination.
Congress may consider whether to more specifically account for how agencies use data standards
that are not voluntary consensus standards, including those that could be unique to the federal
government.
Voluntary consensus standards may be impractical for federal data in certain ways, which the
NTTAA anticipates.185 Some federal data may be unique to federal operations and have no private
sector equivalent that would be subjected to standards. For example, GAO reported that OMB
and HHS did not incorporate voluntary consensus standards into data standards required by the
Grant Reporting Efficiency and Agreements Transparency Act of 2019 because doing so was
“neither reasonable nor practicable.”186 This accords with the law: The act specifies use of
voluntary consensus standards to the extent reasonable and practicable.187 Some believe that in
some situations the federal government may have to set data standards—including for its own
internal data sharing and to reuse data it collects and manages—and should not shy away from
this role.188
OMB has a management and oversight role in IRM policies—which includes data standards for
federal data—and also in technical standards through its guidance to agencies and the reporting it
receives from agencies. While OMB’s Circular A-119 permits agencies to use technical standards
that are not voluntary consensus standards, such as government-unique standards, there is
minimal guidance for using these types of standards. This includes when there is an absence of
voluntary consensus standards or when an agency decides not to use them. For example, NIST
has reported on the absence of data standards for AI systems, including those for data analytics,
data exchange, data accessibility, and data quality.189 At the same time, OMB’s guidance
encourages agencies to adopt voluntary consensus standards for AI “as appropriate and consistent
with OMB Circular No. A-119, if applicable.”190
Currently, agencies are expected to report their use of standards that are not voluntary consensus
standards, including their use of government-unique standards, to OMB under the NTTAA.191
Congress may consider whether government-unique standards apply to federal data and, as such,

184 As it relates to AI, see NIST, U.S. Leadership in AI, p. 13. As it relates to “big data,” see NIST, NIST Big Data
Interoperability Framework: Volume 7, Standards Roadmap (version 3), October 2019, pp. 35-37,
https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-7r2.pdf#page=36.
185 P.L. 104-113, §12(d)(3); 110 Stat. 783.
186 GAO, Grants Management: Action Needed to Ensure Consistency and Usefulness of New Data Standards, p. 22.
187 P.L. 116-103, §4(a); 133 Stat. 3268; 31 U.S.C. §6402(c)(3).
188 Michal S. Gal and Daniel L. Rubinfeld, “Data Standardization,” New York University Law Review, vol. 94, no. 4
(October 2019), p. 769.
189 NIST, U.S. Leadership in AI, p. 11.
190 OMB, M-24-10, p. 6.
191 P.L. 104-113, §12(d)(3); 110 Stat. 783. The NTTAA requires reporting to OMB, and OMB, in turn, directs agencies
to submit annual reports to NIST, which then reports the use of government-unique standards in lieu of voluntary
consensus standards to OMB (OMB, Circular A-119, pp. 33-34).
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whether agencies should be required to comply with reporting requirements to OMB. Congress
may also consider what purpose is served through such a reporting regime and how submitting
annual reports contributes to how agencies substantively use data standards and to federal data
management.
Designating a Role for NIST
Congress may also consider NIST’s role in data standards. Currently, NIST coordinates technical
standards for federal agencies. Designating a specific role for NIST may echo its previous role in
establishing policies and issuing guidance concerned with data standards and the management of
data within agencies. One policy option is to require NIST to coordinate data standards for federal
data, including establishing requirements and guidelines, in a way similar to its responsibilities
for setting federal information system standards,192 of which OMB oversees the development and
implementation.193 The role of NIST could thus be viewed as centralizing a framework for data
standards. Evolving the government-wide role of NIST in data standards, however, is separate
from the potential impacts that organizational culture, staffing, or resourcing have on agencies’
ability to effectively use data standards and other best practices in data management, as discussed
above.
Policy Options for Specifying Data Standards in the Law
For policymakers, it can be a challenge to define the data standards expected for a particular
policy. Some laws, such as the Financial Data Transparency Act of 2022, specifically define data
standard (i.e., “a standard that specifies rules by which data is described and recorded”),194 and
some may even specify various requirements for the data standard, including some technical
requirements.195 While such specification may clarify ambiguities, over-specification may have
unforeseen consequences in practice.196 Technical specifications may pose challenges where data
interoperability is a policy objective.
Implications for Data Interoperability
Specifying technical details for data standards may affect the interpretation of their requirements,
which may affect a policy’s implementation and outcomes. These effects may be more notable
where data are moving beyond typical agency boundaries (e.g., data interoperability; data
integration). First, some stakeholders believe that Congress’s legislative approach should be more
principles-based and less prescriptive, focusing on processes and goals for how federal data are
used.197 Second, the terminology for data standards in cases of data integration and data
interoperability is “not as clear as most people and organizations seem to assume.”198 These issues

192 15 U.S.C. §278g-3(a)(1).
193 44 U.S.C. §3504(h)(1)(B).
194 P.L. 117-263, §5811; 136 Stat. 3422; 12 U.S.C. §5334(a)(3).
195 P.L. 117-263, §5811; 136 Stat. 3422; 12 U.S.C. §5334(c)(1)(B).
196 For example, HHS claims that it cannot develop an improper payment estimate for the TANF program because
statute permits HHS to collect only the data elements specified in Section 411 of the Social Security Act (42 U.S.C.
§611) (see GAO, COVID-19: Current and Future Federal Preparedness Requires Fixes to Improve Health Data and
Address Improper Payments, GAO-22-105397, April 2022, p. 325, https://www.gao.gov/assets/d22105397.pdf#page=
337).
197 GSA, “The U.S. Data Federation Framework,” https://federation.data.gov/us-data-federation-framework/#the-data-
federation-playbook.
198 Lampathaki et al., “Business to Business Interoperability,” p. 1047.
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may present a challenge to the implementation of data standards, because it is a process that
depends on people and organizations. A policy that relies heavily on technical specifications to
enable data interoperability may miss an important observation:
The hardest interoperability problems often have nothing to do with making technologies
work together or enabling data to flow across systems. The hardest problems arise in
linking together entire business processes and workflows across otherwise uncoordinated
people and organizations.199
For example, the Bipartisan Budget Act of 2018 required data exchange standards for maternal,
infant, and early childhood home visiting programs to improve interoperability.200 These data
standards would operate in an environment where data are collected by state government agencies
and local providers.201 HHS, which oversees the programs, found that the data exchange
standards required by the act would be useful for states by supporting program operations,
including for coordinating services provided to program participants, and for program evaluation.
Nonetheless, HHS also found that certain activities are needed before the development of the
standards, including establishing data governance to set the goals for data interoperability,
ensuring that state-level information systems can support data interoperability, identifying a
method for matching data across information systems within a state, and funding the adoption and
use of federal data exchange standards.202 Thus, use of such data exchange standards might be
subordinate to other issues at play in data interoperability.
Certain data standards do not exist in some instances. For example, NIST identified several gaps
in the standards needed for its Big Data Interoperability Framework, including for metadata
specification, sharing and exchanging data, data quality and integrity, and general data
management.203
Relying on standards alone will probably be insufficient to address some of the barriers to data
interoperability. Congress may consider addressing some of these barriers and non-technical
issues, such as the goals of data interoperability, the governance and advisory structures necessary
for achieving such outcomes, and oversight of the efforts to produce those results.
For example, the Geospatial Data Act of 2018 established an interagency data committee to lead
the development, management, and operations of national data infrastructure for geospatial data,
which is to ensure the interoperability and sharing capabilities of federal information systems and
data.204 One of the duties of the committee is to establish and maintain geospatial data
standards,205 with the law also delineating several requirements for such standards206 and
requiring agencies covered by the law to use these standards.207 Additionally, the law created an
advisory committee—with membership to be selected from groups involved in the geospatial

199 Palfrey and Gasser, Interop, p. 52.
200 P.L. 115-123, §50606(a); 132 Stat. 230; 42 U.S.C. §711(h)(5).
201 Ivy Pool, Paul Wormeli, and Daniel Stein, Developing Data Exchange Standards for MIECHV Home Visiting
Programs, September 2020, p. 22, https://www.acf.hhs.gov/sites/default/files/documents/opre/
miechv_des_listening_session_summary_03sep20.pdf#page=25.
202 Pool, Wormeli, and Stein, Developing Data Exchange Standards, pp. 21-27 and 36-37.
203 NIST, NIST Big Data Interoperability Framework: Volume 7, pp. 36-37, https://nvlpubs.nist.gov/nistpubs/
SpecialPublications/NIST.SP.1500-7r2.pdf#page=37.
204 P.L. 115-254, §753; 132 Stat. 3415; 43 U.S.C. §2802; P.L. 115-254, §755(b)(1)(E); 132 Stat. 3421; 43 U.S.C.
§2804(b)(1)(E). The act is Subtitle F, Title VII, of the FAA Reauthorization Act of 2018.
205 P.L. 115-254, §753(c)(3); 132 Stat. 3416; 43 U.S.C. §2802.
206 P.L. 115-254, §757; 132 Stat. 3423; 43 U.S.C. §2806.
207 P.L. 115-254, §759(a)(6); 132 Stat. 3425; 43 U.S.C. §2808(a)(6).
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community—to provide recommendations on implementing the national data infrastructure.208
Specific oversight mechanisms included in the act are inspector general audits that address
compliance by covered agencies with the geospatial data standards and are to be submitted to
Congress209 and evaluation by OMB of covered agencies’ budget justifications relative to their
reported progress to meet their responsibilities under the law.210

Author Information

Natalie R. Ortiz

Analyst in Government Organization and
Management

Disclaimer
This document was prepared by the Congressional Research Service (CRS). CRS serves as nonpartisan
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208 P.L. 115-254, §§754(b)(1), 754(e)(1); 132 Stat. 3418-3419; 43 U.S.C. §§2803(b)(1), 2803(e)(1).
209 P.L. 115-254, §759(c)(1); 132 Stat. 3426; 43 U.S.C. §2808(c)(1).
210 P.L. 115-254, §759(b)(4); 132 Stat. 3426; 43 U.S.C. §2808(b)(4). For more on the Geospatial Data Act of 2018, see
CRS Report R45348, The Geospatial Data Act of 2018, by Peter Folger.
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