U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF INSPECTOR GENERAL
Compliance with the law
Operating efficiently and effectively
EPA's Fiscal Year 2019
First Quarter Compliance
with the Digital
Accountability and
Transparency Act of 2014
Report No. 20-P-0026 November 8, 2019
Sub-award
Attributes
(File F)
Additional
Awardee
Attributes
(File E)
Appropriations
Account
(File A)
Public Website:
USAspending.gov
DATA Act Reporting
Award and Awardee
Attributes
[Procurement] (File Dl)
[Financial Assistance]
(File D2)
Object Class and
Program Activity
(File B)
Award Financial
(FileC)
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Report Contributors:
Abbreviations
Paul Curtis
Claire McWilliams
Kevin Haas
Jennifer Hutkoff
Shannon Lackey
Ryan Watren
Shannon Vos
AAC
Activity Address Code
CFDA
Catalog of Federal Domestic Assistance
CIGIE
Council of the Inspectors General on Integrity and Efficiency
DAIMS
DATA Act Information Model Schema
DATA Act
Digital Accountability and Transparency Act of 2014
DEAR
DATA Act Evaluation and Approval Repository
DQP
Data Quality Plan
EAS
EPA Acquisition System
EPA
U.S. Environmental Protection Agency
FABS
Financial Assistance Broker Submission
FAEC
Federal Audit Executive Council
FAIN
Federal Award Identification Number
FAR
Federal Acquisition Regulations
FFATA
Federal Funding Accountability and Transparency Act of 2006
FPDS-NG
Federal Procurement Data System - Next Generation
FY
Fiscal Year
GAO
U.S. Government Accountability Office
IG
Inspector General
IGMS
Integrated Grants Management System
NAICS
North American Industry Classification System
OGD
Office of Grants and Debarment
OIG
Office of Inspector General
OMB
Office of Management and Budget
PUD
Procurement Instrument Identifier Number
SAO
Senior Accountable Official
Cover image: Graphic illustrating the EPA's DATA Broker System files being submitted for
publication on USAspending.gov. (EPA OIG image)
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At a Glance
Why We Did This Project
The Digital Accountability and
Transparency Act of 2014
(DATA Act) requires the
Inspector General to review a
statistically valid sample of the
spending data submitted under
the act by the U.S.
Environmental Protection
Agency (EPA) and to assess
the completeness, accuracy,
timeliness and quality of the
data sampled and the
implementation and use of the
data standards.
We performed this audit to
assess the completeness,
accuracy, timeliness and
quality of fiscal year (FY) 2019
first quarter financial and award
data submitted to
USAspending.gov by the EPA's
Office of the Chief Financial
Officer, and to assess the
EPA's implementation and use
of the governmentwide financial
data standards established by
the Office of Management and
Budget (OMB) and U.S.
Department of the Treasury
(Treasury).
This report addresses the
following:
Compliance with the law.
Operating efficiently and
effectively.
Address inquiries to our public
affairs office at (202) 566-2391 or
OIG WEBCOMMENTS@epa.gov.
EPA's Fiscal Year 2019 First Quarter
Compiiance with the Digital Accountability
and Transparency Act of 2014
What We Found
We found that the EPA's 2019 first quarter financial
and award data was of "higher" quality as defined by
the DATA Act audit guide issued by the Council of
the Inspectors General on Integrity and Efficiency.
The DATA Act requires
the EPA to report
accurate financial and
award data on
USAspending.gov.
The DATA Act audit guide defines data as being of
higher, moderate or lower quality based on the highest error rate found in testing
the completeness, accuracy and timeliness of data submitted.
We found inconsistencies in processing data that created reporting errors in
completeness, accuracy and timeliness for DATA Act reporting purposes. We
also found that the EPA did not have documented standard operating policies
and procedures for DATA Act reporting. While we found reporting errors and
some issues with documentation of policies and procedures, overall, the EPA
has complied with the requirements of the DATA Act, submitted financial and
award data to the Treasury Broker on time, and had implemented data standards
as defined by the OMB and Treasury.
Recommendations and Planned Agency Corrective Actions
We recommend that the Chief Financial Officer and the Assistant Administrator
for Mission Support:
1. Develop and document standard operating policies and procedures
specific to the completeness, accuracy, timeliness and quality of the
EPA's DATA Act reporting (consistent with DATA Act requirements).
These procedures should also define roles and responsibilities for
performing validation procedures.
2. Continue to coordinate with Treasury to eliminate inconsistent use of
OMB and Treasury-established data standards.
The agency agreed with both recommendations and provided acceptable
planned corrective actions. We consider the recommendations resolved with
corrective actions pending.
List of OIG reports.
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
OFFICE OF
INSPECTOR GENERAL
November 8, 2019
MEMORANDUM
SUBJECT: EPA's FY 2019 First Quarter Compliance with the Digital Accountability and
Transparency Act of 2014
Report No. 20-P-0026
FROM: Paul C. Curtis, Director ^V""'***
Financial Directorate
Office of Audit and Evaluation
TO: David Bloom, Acting Chief Financial Officer
Donna Vizian, Principal Deputy Assistant Administrator
Office of Mission Support
This is our report on the subject audit conducted by the Office of Inspector General (OIG) of the
U.S. Environmental Protection Agency (EPA). The project number for this audit was OA&E-FY 19-0124.
This report contains findings that describe the problems the OIG has identified and corrective actions the
OIG recommends. This report represents the opinion of the OIG and does not necessarily represent the
final EPA position. Final determinations on matters in this report will be made by EPA managers in
accordance with established audit resolution procedures.
The Office of the Chief Financial Officer has primary responsibility for the implementation of the
Digital Accountability and Transparency Act of 2014. Other EPA offices with responsibility for file
submissions for the DATA Act include the Office of the Controller and the Office of Mission Support's
Office of Acquisition Solutions and Office of Grants and Debarment.
In accordance with EPA Manual 2750, your office provided acceptable corrective actions and milestone
dates in response to OIG recommendations. All recommendations are resolved and no final response to
this report is required. However, if you submit a response, it will be posted on the OIG's website, along
with our memorandum commenting on your response. Your response should be provided as an Adobe
PDF file that complies with the accessibility requirements of Section 508 of the Rehabilitation Act of
1973, as amended. The final response should not contain data that you do not want to be released to the
public; if your response contains such data, you should identify the data for redaction or removal along
with corresponding justification.
^tDSX
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We will post this report to our website at www.epa.gov/oig.
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EPA's FY 2019 First Quarter Compliance
with the Digital Accountability and
Transparency Act of 2014
20-P-0026
Table of C
Chapters
1 Introduction 1
Purpose 1
Background 1
Responsible Offices 5
Scope and Methodology 5
Assessment of Internal Controls 7
2 EPA Complied with DATA Act, but Errors Affected Data Quality 8
Completeness and Timeliness of Agency Submission 8
Accuracy of Agency Submission (Files A, B and C) 8
Connecting File C and Files D1/D2 by Award ID Numbers 9
Sample Results for Files C and D1/D2 10
Testing Limitations for Data Reported from Files E and F 13
Supplemental Results 13
Implementation and Use of the Data Standards 19
Conclusion 20
Recommendations 20
Agency Response and OIG Evaluation 20
Status of Recommendations and Potential Monetary Benefits 21
Appendices
A DAIMS Information Flow Diagram 22
B CIGIE's DATA Act Anomaly Letter 23
C Results of Statistical Sample Testing by Record 25
D EPA's Results for the Data Elements 34
E Analysis of the Accuracy of Dollar-Value-Related Data Elements 36
F Agency Response to Draft Report 37
G Distribution 39
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Chapter 1
Introduction
Purpose
Our objectives were to determine whether the U.S. Environmental Protection
Agency's (EPA's) submission of financial and award data for fiscal year (FY)
2019 first quarter complied with the Digital Accountability and Transparency Act
of 2014 (DATA Act) and to assess:
The completeness, accuracy, timeliness and quality of the financial and
award data submitted for publication on USAscending, gov1.
EPA's implementation and use of the governmentwide financial data
standards established by the Office of Management and Budget
(OMB) and the U.S. Department of the Treasury (Treasury).
Background
DATA Act
The DATA Act (Pub. L. 113-101), signed on May 9, 2014, requires federal
agencies to report financial and award data in accordance with the established
governmentwide financial data standards. The DATA Act states that it expands
the Federal Funding Accountability and Transparency Act of2006 (FFATA) by
"disclosing direct Federal agency expenditures and linking Federal contract, loan
and grant spending information to programs of Federal agencies to enable
taxpayers and policy makers to track Federal spending more effectively." The
OMB and Treasury issued guidance on 57 data definition standards and required
federal agencies to report financial data in accordance with these standards
beginning in May 2017. Based on input from federal agencies and the public, the
OMB and Treasury created the DATA Act Information Model Schema (DAIMS)
to provide an authoritative, comprehensive source of governmentwide data terms,
standards and guidance. The information flow diagram in Appendix A provides
timeframes and sources of the data included in the DAIMS.
Each federal agency must submit, at a minimum, quarterly2 spending data to
Treasury in the specific format as shown below in Table 1. The EPA submits data
1 USAspending.gov is the official source for spending data for the U.S. Government. It shows the American public
how taxpayer money is being used.
2 The DAIMS requires a quarterly submission to the DATA Act Broker, twice-monthly award submission to the
Financial Assistance Broker and daily procurement award submission to the Federal Procurement Data System -
Next Generation.
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for Files A, B and C from its financial system through Treasury's DATA Act
Broker (the Broker). The Broker runs a series of validations and produces
warnings and error reports for agencies to review. The Broker also extracts
procurement and financial assistance data from the award submission to populate
Files Dl, D2, E and F.
Table 1: DATA Act submission3
File
Name
File Contents
File Description
EPA-uploaded data from agency financial system
File A
Appropriations
Account Detail
File A includes fiscal year cumulative federal
appropriation account summary-level data.
FileB
Object Class
and Program
Activity Detail
File B includes fiscal year cumulative federal object
class and program activity summary level data.
FileC
Award
Financial Detail
File C includes the obligation amounts for awards
made and/or modified during the reporting period.
Broker-extracted data from external award system
File Dl
Award and
Awardee
Attributes
(Procurement)
File Dl contains detailed information for record
level procurement transactions reported in File C.
File D2
Award and
Awardee
Attributes
(Financial
Assistance)
File D2 contains detailed information for record
level financial assistance transactions reported in
File C.
FileE
Additional
Awardee
Attributes
File E contains detailed information for record level
transactions reported in File C.
FileF
Sub-award
Attributes
File F contains detailed information for record level
transactions reported in File C.
Source: Office of Inspector General (OIG) analysis of CIGIE Federal Audit Executive Council
Inspectors General Guide to Compliance under the DATA Act, 2/14/2019 (DATA Act Audit Guide).
The Chief Financial Officer is the agency's Senior Accountable Official (SAO).
The SAO certifies the submission to attest that the agency's internal controls
provide assurance that the data is valid and reliable. The certified data is displayed
on USAspending.gov. Figure 1 illustrates how information from agencies is
collected and made available to the public.
3 The EPA-uploaded data is submitted from the agency's financial system. The Broker-extracted data is submitted
by an external award reporting system to the Broker.
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Figure 1: Operation of the Broker for quarterly submissions
DATA Act Broker
File E:
File A
Flie 02
File D1:
Additional
awardee
attributes
Appropriations
Account
Procurement
Financial
assistance
File B:
File F:
Object Class
and program
activity
Sub-award
attributes
FileC:
Award
financial
File D1 is
extracted from the
Federal Procurement
Data System
(FPDS-NG)
Checks include field length
and data type to help ensure
consistency and comparability
of data
Validation of data
in selected Files
Agency
certification
File D2 from the Award
Submission Portal (ASP)
File E from the System for Award
Management (SAM)
File F from the Federal Funding
Accountability and Transparency Act
Subrecipient Reporting System (FSRS)
Senior Accountable Officials (SAO)
document their assurance of data
reliability and accuracy
Treasury
database
Storage architecture designed to hold
data extracted from transactional
systems, operational data stores, and
external sources
~
~
Data submission from
agencies
Data extracted from existing
award data system
Beta.USAspending.gov
PENDING
Source: U.S. Government Accountability Office (GAO) report, GAO 18-138; DATA Act: Data Quality and Transparency.
The DATA Act also requires the Office of Inspector General (OIG) of each
federal agency to review a statistically valid sample of the spending data and to
submit a report to Congress assessing the completeness, accuracy, timeliness and
quality of the data, and the implementation and use of the governmentwide
financial data standards.
The Council of the Inspectors General on Integrity and Efficiency's (CIGlE's)
Federal Audit Executive Council (FAEC) DATA Act Working Group released its
Inspectors General Guide to Compliance Under the DATA Act (DATA Act Audit
Guide) on February 14, 2019. The guide provides a baseline framework for the
required reviews and a common methodology and reporting approach to use in
performing work mandated by the DATA Act. We conducted our audit in
accordance with the DATA Act Audit Guide.
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OMB Guidance
OMB Memorandum M-15-12, Increasing Transparency of Federal Spending by
Making Federal Spending Data Accessible, Searchable, and Reliable, dated
May 8, 2015, provides guidance to federal agencies on reporting requirements
under the FFATA and the DATA Act.
OMB Management Procedures Memorandum 2016-03, Additional Guidance for
DATA Act Implementation: Implementing Data-Centric Approach for Reporting
Federal Spending Information, dated May 3, 2016, provides additional guidance
to federal agencies on reporting federal appropriations and award-level data to
USAspending.gov.
OMB Memorandum M-17-04, Additional Guidance for DATA Act Implementation:
Further Requirements for Reporting and Assuring Data Reliability, dated
November 4, 2016, defines responsibilities for reporting financial information for
awards involving intragovernmental transfers. It also provides guidance for
reporting financial assistance award (grant) records containing personally
identifiable information and the requirements for the agency's SAO to certify
quarterly submissions to USAspending.gov.
OMB Memorandum M-18-16, Appendix A to OMB Circular No. A-123,
Management of Reporting and Data Integrity Risk, dated June 6, 2018, includes a
specific requirement for agencies to develop a Data Quality Plan (DQP) to achieve
the objectives of the DATA Act beginning in FY 2019 and continuing through FY
2021 at a minimum, or until agencies determine that they can provide reasonable
assurances over the appropriate data quality controls.
C/G/E Strategy
CIGIE identified a timing anomaly with the oversight requirements contained in
the DATA Act. That is, the first Inspector General (IG) reports were due to
Congress on November 2016; however, federal agencies were not required to
report spending data until May 2017. To address this reporting date anomaly, the
IGs provided Congress with their first required reports by November 8, 2017, one
year after the statutory due date, with subsequent reports to be submitted
following on a 2-year cycle.
On December 22, 2015, CIGIE's chair issued a letter detailing the strategy for
dealing with the IG reporting date anomaly and communicated the strategy to the
Senate Committee on Homeland Security and Governmental Affairs and the
House Committee on Oversight and Government Reform. That letter is in
Appendix B, CIGIE's DATA Act Anomaly Letter.
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Responsible Offices
The EPA's Office of the Chief Financial Officer maintains responsibility for the
EPA's implementation of the DATA Act. The Chief Financial Officer is the SAO
who approves and provides assurance that the data submission is valid and
reliable. Other EPA offices with responsibility for file submissions for the DATA
Act include the Office of the Controller the Office of Mission Support's Office of
Acquisition Solutions and Office of Grants and Debarment (OGD).
Scope and Methodology
We conducted this audit from March 2019 through November 2019 in accordance
with generally accepted government auditing standards issued by the Comptroller
General of the United States. Those standards require that we plan and perform
the audit to obtain sufficient, appropriate evidence to provide a reasonable basis
for our conclusions based on our audit objectives. We believe that the evidence
obtained provides a reasonable basis for our findings and conclusions based on
our audit objectives.
To accomplish our objectives, the audit team sought to:
Obtain an understanding of any regulatory criteria related to the EPA's
responsibilities to report financial and award data under the DATA Act.
Review the EPA's DQP.
Assess the internal control and information system controls in place as
they relate to the extraction of data from the source systems and the
reporting of data to the Broker to assess audit risk and design audit
procedures4.
Review and reconcile the FY 2019 first quarter summary-level data
submitted by the agency for publication on USAspending.gov.
Assess the completeness, accuracy, timeliness and quality of the financial
and award data sampled.
Assess the EPA's implementation and use of the 57 data
elements/standards established by the OMB and Treasury.
We selected 332 transactions for data element testing based on the DATA Act
submission population of 2,403 first quarter 2019 transactions following the
DATA Act Audit Guide's sampling methodology.5 We used stratified random
sampling to select the transactions to test contracts and grants.
4 OMB Circular A-123, Management's Responsibility for Enterprise Risk Management and Internal Control (July 15,
2016) and Appendix A to OMB Circular A-123, Management of Reporting and Data Integrity Risk (June 6, 2018).
5 For agencies with a smaller population (385/2,403 > 5%) and high expected error rates (50%), where the
recommended sample size of 385 represents 5 percent or more of the population, the DATA Act Audit Guide
suggests that the IG may reduce the sample size by applying the finite correction factor using the following formula
to determine the recommended sample size: 385/[l+(385/N)], where "N" represents the population size
[385/[l+(385/2,403)] = 332],
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Prior Audit Coverage
EPA OIG Report No. 18-P-0037. EPA Reported Its Fiscal Year 2017 Second
Quarter Financial and Award Data in Accordance With the DATA Act, issued
November 9, 2017, found that the EPA assessed the completeness, accuracy,
timeliness and quality of the FY 2017 second quarter financial and award data
submitted for publication on USAspending.gov. The EPA also had implemented
governmentwide financial data standards established by the OMB and Treasury.
EPA OIG Report No. 17-P-0050. Status of EPA's Implementation of the DATA
Act, issued December 2, 2016, found that the EPA had taken steps to implement
the DATA Act. The EPA identified key risks to the DATA Act implementation,
such as linking award identification among the EPA's financial and procurement
systems; submitting complete data files to Treasury; timing differences, data
inconsistencies and reconciling data between the EPA internal and external
systems; and funding to support the consolidation and preparation of agency data
for reporting under the DATA Act. The EPA developed an implementation plan
to mitigate these risks. We had no recommendations since that implementation
plan was designed for the EPA to meet the statutory deadline with a partial data
submission.
The GAO issued reports regarding the DATA Act, including:
DATA Act: OMB Needs to Formalize Data Governance for Reporting
Federal Spending (GAO-19-2841 issued March 22, 2019.
Open Data: Treasury Could Better Align USAspending.gov with Key
Practices and Search Requirements (GAO-19-72). issued December 13,
2018.
DA TA Act: Reported Quality of Agencies' Spending Data Reviewed by
OIGs Varied Because of Government-wide and Agency Issues
(GAO-18-5461 issued July 23, 2018.
DATA Act: OMB, Treasury, and Agencies Need to Improve Completeness
and Accuracy of Spending Data and Disclose Limitations (GAO 18-138).
issued November 8, 2017.
DA TA Act: As Reporting Deadline Nears, Challenges Remain That Will
Affect Data Quality (GAO-17-496). issued April 28, 2017.
Data Transparency: Oversight Needed to Address Underreporting and
Inconsistencies on Federal Award Website (GAO-14-476). issued June 30,
2014.
Electronic Government: Implementation of the Federal Funding
Accountability and Transparency Act of2006 (GAO-10-365), issued
March 12, 2010.
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Assessment of Internal Controls
The FY 2018 financial statement audit assessed Compass, the agency's financial
management system. We relied on internal control testing conducted for the
EPA's FY 2018 financial statement audit and believe that the EPA's internal
controls related to the DATA Act are effective and that the agency can certify
with reasonable assurance that the data is complete, accurate and timely. During
the financial statement audit, no material weaknesses or management challenges
were found that would impact the internal controls that EPA relies on for the
DATA Act.
The Broker interfaces with Compass through the DATA Act Evaluation and
Approval Repository (DEAR), a tool that extracts, transforms and prepares data.
EPA also uses DEAR to reconcile data and validate DATA Act Files A, B and C
for submission to the Broker. The DEAR tool performs edit checks and generates
exception reports. The validation check files and warning lists produced by the
DEAR tool must be addressed before the data can be submitted to the Broker. The
generation of these files and the agency's correction of file warnings show the
effectiveness of the internal controls related to the DEAR tool.
Enterprise Risk Management Plan
The EPA's risk profile for 2018 lists acquisition/contracting, human capital and
resources as the agency's enterprise risks. These risks have no direct impact on
controls over DATA Act source systems and reporting.
Agency's DATA Act Assurance Statement
The EPA DA TA Act Assurance Statement and DA TA Act Evaluation and
Approval Repository Certification, FY 2019, 1st Quarter, March 2019 (Assurance
Statement), certified the agency complied with OMB Memorandum M-17-04,
Additional Guidance for DATA Act Implementation: Further Requirements for
Reporting and Assuring Data Reliability. Along with the certification of
compliance, the agency's statement documented certain source data anomalies,
including historical program activity code non-compliance, timing issues and
business process limitations.
Data Quality Plan
Pursuant to OMB Circular No. A-123, Appendix A, Management of Reporting
and Data Integrity Risk, agencies are required to develop a DQP in FY 2019. EPA
finalized its DQP on September 30, 2019. We did not consider the DQP in our
analysis of internal controls because it was not available during the audit.
20-P-0026
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Chapter 2
EPA Complied with DATA Act,
but Errors Affected Data Quality
We found that the EPA has implemented the required DATA Act data elements
and transmitted its submission on time. However, internal control weaknesses
affected the accuracy of the agency's submission of Files C and D1/D2. These
weaknesses included a lack of documented policies and procedures, which
resulted in errors in the data files included in its DATA Act submission for the FY
2019 first quarter.
Completeness and Timeliness of Agency Submission
We evaluated the agency submission (Files A, B and C) for completeness and
timeliness. The DATA Act Audit Guide provides the following definitions:
Completeness of Agency Submission: Transactions and events that should
have been recorded are recorded in the proper period.
Timeliness of Agency Submission: Reporting of the agency DATA Act
submission to the Broker is in accordance with the schedule established by
the Treasury DATA Act Project Management Office.
We evaluated the EPA's DATA Act submission to the Broker and determined that
the submission was complete and submitted on time, before the revised deadline
of March 20, 20196. We evaluated Files, A, B and C to determine that all
transactions and events were recorded in the proper period. Our test work did not
identify any significant variances.
Accuracy of Agency Submission (Files A, B and C)
Files A, B and C originate from Compass. Through our reconciliations and test
work, we determined that Files A and B are accurate and the data links properly to
File C. The agency has adequately reconciled Files A and B with each other and
with the GTAS7 SF-133, Report on Budget Execution and Budgetary Resources8.
The agency has adequately reconciled Files B and C with each other.
6 The normal deadline set by U.S. Treasury is 45 days after the end of the quarter, which would have been February
15, 2019, for submission of FY 2019 first quarter data. Because of the 35-day lapse in appropriations from
December 22, 2018-January 25, 2019, Treasury changed the deadline to March 20, 2019.
7 The Governmentwide Treasury Account Symbol Adjusted Trial Balance System (GTAS) is the system used by
agencies to report budget execution information and proprietary financial reporting information to Treasury.
8 The SF-133 is a quarterly report that contains information on the sources of budget authority and the status of
budgetary resources by individual fund or appropriation.
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Appropriation Account (File A)
The appropriation account file (File A) includes cumulative federal appropriations
account summary-level data for the fiscal year. The data from the appropriations
account summary-level data from File A and the agency's GTAS SF-133, Report
on Budget Execution and Budgetary Resources were aligned.
Object Class and Program Activity (File B)
The object class9 and program activity10 file (File B) includes cumulative federal
object class and program activity summary-level data for the fiscal year. Based on
our analysis, the agency has adequately reconciled File A with File B.
Furthermore, we determined that all object class codes from File B match the
codes defined in OMB Circular A-l 1, Preparation, Submission, and Execution of
the Budget.
Award Financial Detail (File C)
The award financial detail file (File C) includes obligation amounts for awards
made and/or modified during the reporting period. The auditors matched each
Treasury Symbol, Program Activity Code and Object Class in File C to File B.
Connecting File C and Files D1/D2 by Award ID Numbers
Files D1 and D2 are submitted by external award reporting systems to the Broker
and contain detailed information for the record level transactions reported in File
C. File C and Files D1 and D2 link together by the Award Identification (Award
ID) Number. We compared Award ID Numbers in File C to those in File D1/D2 to
determine whether the files contained the same Award ID Number and found
discrepancies as detailed below.
Award and Awardee Attributes (Procurement - File D1)
We found that all Award ID Numbers in File C were in File Dl; however, not all
Award ID Numbers in File Dl were in File C. File C was missing records because
it is submitted by the EPA from Compass, and the Broker creates File Dl
primarily from the EPA Acquisition System (EAS). Different offices within the
EPA manage the financial system and award systems, which results in timing
differences and manual input errors.
As reported in the EPA's internal analysis and referenced by the EPA's FY 2019
first quarter Assurance Statement, discrepancies were primarily a result of the
following timing difference conditions:
9 Object class refers to goods or services or items purchased. For example, supplies, rent or equipment.
10 Program activity refers to activity, project or other programmatic distinction.
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Transactions at the end of the first quarter in the award systems that were
processed at the beginning of the second quarter in the financial system.
Transactions for which documents had not been sent by the project officer to
be processed by the finance center.
Transactions that were not recorded on time by the finance center due to data
entry errors.
After the agency completed the DATA Act submission, the EPA analyzed and
reported causes of errors in Files C, D1 and D2. We verified the differences found
by the agency and analyzed the errors. Based on our analysis, we determined that
File C was suitable for sample testing.
Award and Awardee Attributes (Financial Assistance - File D2)
When comparing File C to File D2, we found that all records in File D2 were in
File C, but not all records in File C were in File D2.
Records missing from File D2 resulted from transactions that were processed only
in the financial system outside of the awards system. These transactions are
primarily financial closeouts and the corresponding deobligations, as the agency
noted in its Assurance Statement. The errors are included in our summary of
sample results in Appendix C, Results of Statistical Sample Testing by Record, to
provide an accurate projection of errors.
Sample Results for Files C and D1/D2
We tested File C federal award transactions by selecting a stratified random
sample of 332 records (252 contracts and 80 grants11), consisting of up to 46 data
elements for contract samples and up to 45 data elements for grant samples for
completeness, accuracy and timeliness (see definitions in Table 2).
11 We stratified the population based on percentage of Procurement Instrument Identifier Numbers (PIIDs) and
Federal Assistance Identifier Numbers (FAINs). PIIDs are unique numbers for contracts, and FAINs are unique
numbers for grants or assistance agreements. Out of 2,403 records in File C, there were 1,827 contracts (76%) and
576 grants (24%). Out of 332 samples, we extracted a stratified random sample of 252 contracts (76% x 332) and 80
grants (24% x 332).
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Table 2: Definition of data elements
Term
Definition
Completeness of
Data Elements
For each of the required data elements that should have
been reported, the data element was reported in the
appropriate Files A through D2.
Accuracy of Data
Elements
Amounts and other data relating to recorded
transactions have been recorded in accordance with the
DAIMS, Reporting Submission Specification, Interface
Definition Document, and the online data dictionary
and agree with the authoritative source records.
Timeliness of Data
Elements
For each of the required data elements that should have
been reported, the data elements were reported in
accordance with the reporting schedules defined by the
financial, procurement and financial assistance
requirements (FFATA, Federal Acquisition
Regulations [FAR], Federal Procurement Data System
- Next Generation [FPDS-NG12], Financial Assistance
Broker Submission [FABS13] and DAIMS).
Source: DATA Act Audit Guide.
We found that 27 of the 80 grant samples extracted from File C did not have a
corresponding File D2 record. As a result, the 27 samples are considered errors
for completeness, accuracy and timeliness.
Projected Error Rate Calculation
We projected error rates for each attribute to evaluate completeness, accuracy and
timeliness. Projected error rates estimate the effect of the errors on all DATA Act
records. These projected error rates are automatically calculated using embedded
formulas in the DATA Act Audit Guide, Appendix 7, Testing Spreadsheet Tool.
The total projected error rate for all samples by attribute is calculated using the
following formula and is expressed as a percentage.
Sum of Error Rates for Attribute at the Record Level
Total Number of Sample Records (332)
= Total Projected Error Rate for Attribute
All error rates for completeness, accuracy and timeliness at the sample level and
the calculation of total projected error rates can be found in Appendix C, Results
of Statistical Sample Testing by Record.
12 FPDS-NG provides procurement data to USAspending.gov. EAS is fully integrated with FPDS-NG and GSA's
Integrated Award Environment, which manages the federal acquisition and awards processes in online systems,
which are now being merged into one. The consolidated system, beta.SAM.gov, will become the official U.S.
government website for people who make, receive and manage federal awards.
13 The agency submits data to Treasury's FABS system twice a month using the DAIMS Reporting Submission
Specification schema.
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Completeness of Data Elements
A data element is complete if the required data element was reported in the
appropriate Files A through D2. The projected error rate for completeness of data
elements is 7.41 percent14. The primary reason for completeness errors are missing
records for 27 grant samples in File D2.
Accuracy of Data Elements
A data element is accurate when amounts and other data relating to recorded
transactions were recorded in accordance with the DAIMS, Reporting Submission
Specification, Interface Definition Document and the online data dictionary, and
agree with the authoritative source records. The projected error rate for accuracy
of data elements is 10.73 percent15. We found a variety of errors in contract and
grant samples, and more accuracy errors in our grant samples.
Timeliness of Data Elements
Timeliness of data elements is based on reporting schedules defined by the
financial, procurement and financial assistance requirements (FFATA, FAR,
FPDS-NG, FABS and DAIMS). The projected error rate for the timeliness of the
data elements is 7.39 percent16. The primary reason for timeliness errors were
missing records for 27 grant samples in File D2.
Quality of Data Elements
The DATA Act Audit Guide defines quality as "[d]ata that is complete, accurate
and reported on a timely basis." Quality of data elements was determined using
the midpoint of the range of the proportion of errors (error rate) for completeness,
accuracy and timeliness. Table 3 from the DATA Act Audit Guide provides
ranges of errors used in determining the overall quality of the data elements.
Table 3: Error ranges
Highest Error Rate
Quality Level
0% - 20%
Higher
21%-40%
Moderate
41% and above
Lower
Source: DATA Act Audit Guide.
14 Based on a 95% confidence level, the projected error rate for the completeness of the data elements is between
5.12% and 10.65%.
15 Based on a 95% confidence level, the projected error rate for the accuracy of the data elements is between 7.95%
and 14.40%.
16 Based on a 95% confidence level, the projected error rate for the timeliness of the data elements is between 5.12%
and 10.65%.
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The total error rate for each attribute for all the samples is listed below:
Completeness: 7.41 percent.
Accuracy: 10.73 percent.
Timeliness: 7.39 percent.
According to the DATA Act Audit Guide, the highest error rate for completeness,
accuracy and timeliness is used to determine the quality level of the data. Based
on our test work, we found the highest error rate to be 10.73 percent, which would
result in the data being classified as of higher (green) quality.
Testing Limitations for Data Reported from Files E and F
File E of the DAIMS contains additional awardee attribute information the Broker
extracts from the System for Award Management, SAM.gov. the official
government website for people who make, receive and manage federal awards.
File F contains sub-award attribute information the Broker extracts from the
FFATA Subaward Reporting System. File E and F data remain the responsibility
of the awardee in accordance with the terms and conditions of federal agreements,
and the quality of this data remains the legal responsibility of the recipient.
Therefore, agency SAOs are not responsible for certifying the quality of File E
and F data reported by awardees, but they are responsible for assuring controls are
in place to verify that grant awardees register in SAM.gov at the time of the
award. As such, we did not assess the completeness, accuracy, timeliness and
quality of the data extracted from SAM.gov and the FFATA Subaward Reporting
System via the Broker system.
Supplemental Results
Figure 2 depicts accuracy error rates, excluding the 27 grant samples that were
missing File D2 data to accurately illustrate the specific data element issues
encountered during the audit.
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Figure 2: Data element accuracy error rates (over 10%)
DE 4: Ultimate Parent Legal Entity Name
DE 5: Legal Entity Address
DE 6: Legal Entity Congressional District
DE 16: Award Type
DE 20: CFDA Title
DE 30: Primary Place of Performance Address
DE 31: Primary Place of Perf. Congressional District
DE38: Funding Agency Name
DE 39: Funding Agency Code
DE 42: Funding Office Name
DE 43: Funding Office Code
DE 48: Awarding Office Name
DE 49: Awarding Office Code
Source: OIG sample analysis.
Data Element Analysis
The following are significant issues we identified regarding reporting data
element information. When determining the level of errors in grants, we
calculated the rate excluding the 27 grant samples that were missing from File D2
to avoid skewing error rates. Comprehensive error rates for each data element can
be found in Appendix D, The EPA's Results for the Data Elements.
Legal Entity Name and Legal Entity Address
For contracts, the FAR, 48 CFR 4.1102 requires those planning to submit an
offer or quote to register on SAM.gov "at the time an offer or quotation is
submitted." The FAEC DATA Act Working Group interprets this to mean the
Legal Entity Name and Legal Entity Address on the base document must
match SAM.gov at the time the base document was signed. Contract data is
input in EAS and then transferred to FPDS-NG for reporting on
USAspending.gov. On original (base or new) contracts, FPDS-NG extracts
from SAM.gov the Legal Entity Name and Legal Entity Address. For amended
contracts, FPDS-NG does not access SAM.gov. We tested the accuracy of
Legal Entity Name and Legal Entity Address for contract samples by
comparing the File D1 Broker data to the award system data and then verified
with SAM.gov. When the name and/or address on SAM.gov did not match
File Dl, we reviewed the historical record on SAM.gov and compared it to the
base contract in EAS. When the historical record on SAM.gov matched the
base contract, we attributed the exception to FPDS-NG. When the historical
ฆ Contracts
ฆ Grants
ฆ Combined
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record on SAM.gov did not match the base contract, we attributed the
exception to the agency.
We tested 252 contracts and found five accuracy exceptions for Legal Entity
Name and 25 accuracy exceptions tor Legal Entity Address, error rates of 2.0
and 9.9 percent, respectively. Two Legal Entity Name exceptions were
attributable to the agency, and three were attributable to FPDS-NG. We found
13 Legal Entity Address exceptions were attributable to the agency, and 12
were attributable to FPDS-NG.
Guidance for grant data on the Federal Spending Transparency website17 for
the DATA Act states, "OMB has determined that SAM will be the
authoritative source for legal entity name and address. Agencies will need to
ensure that this data is in their management systems and exactly matches with
what is in SAM." Grant data is input in the agency's Integrated Grants
Management System (IGMS). This is transferred to FABS for reporting on
USAspending.gov. FABS does not extract name and address information from
SAM.gov. Agencies are responsible for ensuring that award-level data in their
systems match data in SAM.gov at the time of the award and for the duration
of the award. We tested the accuracy of Legal Entity Name and Legal Entity
Address for grant samples by comparing the File D2 Broker data to IGMS
data and then verified with SAM.gov. When the name and/or address on
SAM.gov did not match File D2, we marked it as an exception attributable to
the agency.
We tested 53 existing grant samples that did have a File D2 record and found
two accuracy exceptions to Legal Entity Name and 19 accuracy exceptions to
Legal Entity Address, error rates of 3.8 and 35.8 percent, respectively. All 21
of the name and address exceptions for grant samples were attributable to the
agency.
Ultimate Parent Unique Identifier and Ultimate Parent Legal Entity
Name
We tested the accuracy of the Ultimate Parent Unique Identifier and Ultimate
Parent Legal Entity Name. We identified the parent name, when applicable,
on SAM.gov for the contractor/grantee and then verified whether the parent
unique identifier, a 9-digit number assigned by Dun & Bradstreet, matched the
parent name. We compared this information from SAM.gov to the File D1/D2
Broker data.
We tested 252 contracts and found seven accuracy exceptions for Ultimate
Parent Unique Identifier and 19 accuracy exceptions for Ultimate Parent
Legal Entity Name, error rates of 2.8 and 7.5 percent, respectively.
17 The Federal Spending Transparency website, a sister site ofUSAspending.gov, is a collaboration space designed
to share the process for meeting the data transparency requirements of the DATA Act.
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We tested 53 grants and found no accuracy exceptions for Ultimate Parent
Unique Identifier and 12 accuracy exceptions for Ultimate Parent Legal Entity
Name, an error rate of 22.6 percent. We found that seven of the 12 samples
had no information in the Ultimate Parent Legal Entity Name field, so both
the completeness and timeliness error rates are 13.2 percent (7/53).
Ultimate Parent Unique Identifier and Ultimate Parent Legal Entity Name
fields are derived from SAM.gov. Exceptions to these data elements are not
attributable to the agency.
Non-Federal Funding Amount
We tested the accuracy of the Non-Federal Funding Amount by comparing the
File D2 Broker data with the sum of non-federal funding identified in IGMS.
Elements of non-federal funding in IGMS include contributions from the
recipient, state, local and other sources.
In the samples tested, we found that the EPA only included the recipient
contribution in this field. We found three instances totaling $2,173,448 in
which state, local or other contributions were not included as part of the Non-
Federal Funding Amount. The EPA agreed with this finding and stated they
would make the necessary business process changes. No timeline was
provided, but the OGD stated they would implement the change soon.
Award Type (Assistance Type)
For grants, the data element Award Type consists of the data field Assistance
Type, which is a numerical value identifying the type of assistance, and the
data field Assistance Type Description, which is derived from Assistance
Type. We tested the accuracy of Award Type by comparing the File D2 Broker
data for Assistance Type and Assistance Type Description with the Agreement
Type for the original grants in IGMS.
We tested 53 grants and found 39 accuracy exceptions for Award Type, a rate
of 73.6 percent.
The Award Type on the grant document and in File D2 only matched when the
grant was a cooperative agreement grant. Currently, the EPA assigns the value
for Award Type based on the Catalog of Federal Domestic Assistance (CFDA)
numbers. OGD stated they would review the grants business process in IGMS
and CFDA numbers and will make the necessary changes. No timeline was
provided.
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CFDA Number and CFDA Title
CFDA Titles are derived from the CFDA Number. We tested the accuracy of
the CFDA Number and CFDA Title fields by comparing the File D2 Broker
data with the data in IGMS. We tested 53 grants and found no accuracy
exceptions to the CFDA Number and 15 accuracy exceptions to the CFDA
Title, an error rate of 28.3 percent. CFDA Titles have not been updated in
IGMS. The EPA stated they will make the necessary business process
changes. No timeline was provided.
Period of Performance Start Date
The D AIMS defines the Period of Performance Start Date as "the date on
which, for the award referred to by the action being reported, awardee effort
begins or the award is otherwise effective." For modifications of procurement
awards, it is not clear whether "the award referred to" is the initial award or
the modification, and neither the OMB nor Treasury's DATA Act Program
Management Office has issued guidance with specific instructions on this
matter. Thus, for procurement awards with modifications, it is not an error for
DATA Act reporting purposes if agencies recorded the initial award date or
the date of the modification as the start date, in accordance with their internal
policies and procedures/practices.
Primary Place of Performance Address
The DAIMS defines Primary Place of Performance Address as "the address
where the predominant performance of the award will be accomplished." The
address is made up of the city, state and ZIP+4 code or postal code. The
intention of this field, as indicated on the Federal Spending Transparency
website, is to inform the public where taxpayer money is being spent.
We tested the accuracy of the Primary Place of Performance Address by
comparing the File D1/D2 Broker data with the data in EAS or IGMS. When
comparing IGMS with File D2, we found the Primary Place of Performance
Address was often presented inconsistently. We tested 53 grants and found 35
accuracy exceptions for Primary Place of Performance Address, an error rate
of 66 percent.
The EPA's Primary Place of Performance Address values generally originate
from SAM.gov for each grantee. However, the grantee address is often not the
same as the place of performance indicated on the grant document.
Award ID Number (PUD)
Contracts that were new on or after October 1, 2017, must include the correct
Procurement Instrument Identifier Number (PIID) in the Award ID Number
20-P-0026
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(PUD) field. We tested the accuracy of the Award ID Number (PUD) by
comparing the File D1 Broker data with the data in EAS. We tested 252
contract samples and found 10 instances in which the correct PIID was not in
the Award ID Number (PIID) field18, an error rate of 4 percent. The EPA
stated that they were unable to comply with the new criteria for the PIID
format until April 1, 2018, due to system limitations, and the OMB was aware
of the delayed implementation.
The EPA stated that the Integrated Award Environment, a Presidential E-
Government initiative managed by the General Services Administration
responsible for maintaining and updating FPDS-NG, announced FPDS-NG
implemented the PIID validations on April 1, 2018. The delay in
implementing a validation tool in FPDS-NG does not preclude the agency
from the responsibility to have updated PIIDs by the October 1, 2017,
deadline.
Funding Agency Name, Funding Agency Code, Funding Office
Name and Funding Office Code
Funding Agency Name, Funding Agency Code, Funding Office Name and
Funding Office Code (Funding Office) fields are required for new grants with
action dates on or after October 1, 2018. Eight grant samples fit this criterion.
We attempted to test the accuracy of the Funding Office fields for grant
samples by comparing the File D2 data with the data in IGMS, but all grant
samples were blank for all four required Funding Office fields. This is an error
rate of 100 percent.
The EPA stated that updated Activity Address Codes (AACs) for Funding
Office fields were not provided by GSA until February 2019, at which point
the EPA began implementing the codes, working backward to grants dated
October 1, 2018. The AACs the agency provided to the OIG were current as
of April 1, 2016, and the AACs the agency received from GSA in February
2019 were the same as those dated April 1, 2016. The EPA stated
implementation for these corrections is complete.
Awarding Office Name and Awarding Office Code
We tested the accuracy of the Awarding Office fields for grant award samples
by comparing the File D2 data with the data in IGMS and verified with the
AACs provided by the agency. We tested 53 grants and found 53 accuracy
exceptions for Awarding Office Name and Awarding Office Code fields, an
error rate of 100 percent.
18 The PIID was in the Parent Award ID Number field and a secondary, indistinct number was in the Award ID
Number (PIID) field.
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The EPA coded the Awarding Office Name and Awarding Office Code fields
incorrectly. The values in these fields were identified as Funding Office Name
and Funding Office Code data. The EPA agreed with this finding and stated
the error was corrected.
Analysis of the Accuracy of Dollar-Value-Related Data Elements
The results of our analysis of the accuracy of dollar-value-related data elements
for procurement samples revealed one dollar-value exception of $73,763 in
Transaction Obligated Amount, five dollar-value exceptions totaling $562,538 in
Current Total Value of Award, and four dollar-value exceptions totaling
$2,980,178 in Potential Total Value of Award.
The results of our analysis of the accuracy of dollar-value-related data elements
for financial assistance samples revealed the following:
The 21 dollar-value exceptions totaling $5,714,763 in Federal Action
Obligation were grants that were not recorded in IGMS and did not have any
dollar amount reported in File D2.
Out of the 11 dollar-value exceptions totaling $2,609,632 in Non-Federal
Funding Amount, eight totaling $436,184 were grants that were not recorded
in IGMS and, therefore, did not have any dollar amount reported in File D2.
Three of these, totaling $2,173,448, were instances in which the EPA did not
include all elements of the Non-Federal Funding Amount. This is discussed in
the Data Element analysis above.
Of the 25 dollar-value exceptions totaling $8,324,395 in the Amount of Award,
22, totaling $6,150,947, were grants that were not recorded in IGMS and,
therefore, did not have any dollar amount reported in File D2. Three of these,
totaling $2,173,448, were instances in which the EPA did not include all
elements of the Non-Federal Funding Amount.
Further detail can be found in Appendix E, Analysis of the Accuracy of Dollar-
Value-Related Data Elements.
Implementation and Use of the Data Standards
The EPA has implemented and is using governmentwide financial data standards
for spending information as defined by the OMB and Treasury. However, data
inconsistencies created reporting errors in terms of completeness, accuracy and
timeliness, and reduced the transparency of financial and award data. Specifically,
Files C and D2 were missing records; the absent data elements were from
transactions that were processed only in either the agency's financial system or
awards system. Further, the agency acknowledged that it has not consistently used
the OMB and Treasury-established data elements per its inventory/mapping for
the FY 2019 first quarter data submission. We also found that the EPA did not
20-P-0026
19
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have documented standard operating policies and procedures for DATA Act
reporting.
Conclusion
We conclude that, with few exceptions, the EPA's FY 2019 first quarter data for
publication on USAspending.gov was complete, accurate, timely and of higher
quality. However, we identified specific data inconsistencies that indicate the
EPA could improve internal controls over implementing data standards and
preparing its DATA Act submission.
Recommendations
We recommend that the Chief Financial Officer and the Assistant Administrator
for Mission Support:
1. Develop and document standard operating policies and procedures specific
to the completeness, accuracy, timeliness and quality of the EPA's Digital
Accountability and Transparency Act reporting (consistent with Digital
Accountability and Transparency Act requirements). These procedures
should also define roles and responsibilities for performing validation
procedures.
2. Continue to coordinate with the U.S. Department of the Treasury to
eliminate inconsistent use of Office of Management and Budget and
U.S. Department of the Treasury-established data standards.
Agency Response and OIG Evaluation
The agency agreed with both recommendations and provided acceptable planned
corrective actions. We consider the recommendations resolved with corrective
actions pending. The agency's response can be found in Appendix F.
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Status of Recommendations and
Potential Monetary Benefits
RECOMMENDATIONS
Potential
Planned
Monetary
Rec.
Page
Completion
Benefits
No.
No.
Subject
Status1
Action Official
Date
(in $000s)
20 Develop and document standard operating policies and
procedures specific to the completeness, accuracy, timeliness
and quality of the EPA's Digital Accountability and Transparency
Act reporting (consistent with Digital Accountability and
Transparency Act requirements). These procedures should also
define roles and responsibilities for performing validation
procedures.
20 Continue to coordinate with the U.S. Department of the Treasury
to eliminate inconsistent use of Office of Management and
Budget and U.S. Department of the Treasury-established data
standards.
Chief Financial Officer and 9/30/20
Assistant Administrator for
Mission Support
Chief Financial Officer and 9/30/20
Assistant Administrator for
Mission Support
1 C = Corrective action completed.
R = Recommendation resolved with corrective action pending.
U = Recommendation unresolved with resolution efforts in progress.
20-P-0026
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Appendix
DAI MS Information Flow Diagram
Quarterly Reporting - DATA Act
Daily/Twice-monthlv Reporting - FFATA
a;
"u
C
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Appendix B
CIGIE's DATA Act Anomaly Letter
Council of the
Inspectors General
on INTEGRITY and EFFICIENCY
December 22, 2015
The Honorable Ron Johnson
Chairman
The Honorable Thomas Carper
Ranking Member
Committee on Homeland Security
and Governmental Affairs
United States Senate
Washington, D.C.
Dear Mr. Chairmen and Ranking Members:
The Council of the Inspectors General on Integrity and Efficiency (CIGIE) recognizes and
appreciates your leadership on issues of Government transparency and accountability. In
particular, we believe the enactment last year of the Digital Accountability and Transparency Act
of 2014 (DATA Act) will significantly improve the quality of Federal spending data available to
Congress, the public, and the accountability community if properly implemented. To make sure
this happens, the DATA Act provides for strong oversight by way of the Federal Inspectors
General and the Government Accountability Office (GAO). In particular, the DATA Act
requires a series of reports from each to include, among other things, an assessment ot the
completeness, timeliness, quality, and accuracy of data submitted by agencies under the DATA
Act.
I am writing this letter on behalf of CIGIE to inform you of an important timing anomaly with
the oversight requirement for Inspectors General in the DATA Act. Your staffs have been
briefed on this timing anomaly, which affects the first Inspector General reports required by the
DATA Act. Specifically, the first Inspector General reports are due to Congress in November
2016 However, the agencies we oversee are not required to submit spending data in compliance
with the DATA Act until May 2017. As a result, Inspectors General would be unable to report
on the spending data submitted under the Act, as this data will not exist until the following year.
This anomaly would cause the body of reports submitted by the Inspectors General m November
2016 to be of minimal use to the public, the Congress, the Executive Branch, and others.
To address this reporting date anomaly, the Inspectors General plan to provide Congress with
their first required reports in November 2017, a one-year delay from the due date in statute, with
subsequent reports following on a two-year cycle, in November 2019 and November 2021 We
believe that moving the due dates back one year will enable the Inspectors General to meet the
1717 H Street, NW, Suite 825, Washington, DC 20006
The Honorable Jason Chaffetz
Chairman
The Honorable Elijah Cummings
Ranking Member
Committee on Oversight and Government Reform
U.S. House of Representatives
Washington, D.C.
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Page 2
intent of the oversight provisions in the DATA Act and provide useful reports for the public, the
Congress, the Executive Branch, and others.
Although we think the best course of action is to delay the Inspector General reports, CTGIE is
encouraging the Federal Inspector General Community to undertake DATA Act "readiness
reviews" at their respective agencies well in advance of the first November 2017 report.
Through a working group, CIGIE has developed guidance for these reviews. I am pleased to
report that several Inspectors General have already begun reviews at their respective agencies,
and many Inspectors General are planning to begin reviews in the near future. We believe that
these reviews, which are in addition to the specific oversight requirements of the Act, will assist
all parties in helping to ensure the success of the DATA Act implementation.
\Ve have kept GAO officials informed about our plan to delay the first Inspector General reports
for one year, which they are comfortable with, and our ongoing efforts to help ensure early
engagement through Inspector General readiness reviews.
Should you or your staffs have any questions about our approach or other aspects of our
collective DATA Act oversight activities, please do not hesitate to contact me at (202) 514-J4iD.
Sincerely,
/I i J /' , ซ J i!\ (
-/ . 5 /i , ซ ,1 *\ l\ , f(V \ \
jj at'l ^ 1 ''-A ^
Michael E. Horowitz
Chair, Council of the Inspectors General on Integrity and Efficiency
Inspector General, U.S. Department of Justice
cc: The Honorable David Mader, Controller, OMB
The Honorable Gene Dodaro, Comptroller General, GAO
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Appendix C
Results of Statistical Sample Testing by Record
Sample Record
#
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
1
44
2
4.55%
2
4.55%
0
0.00%
2
42
0
0.00%
0
0.00%
0
0.00%
3
43
0
0.00%
2
4.65%
0
0.00%
4
42
0
0.00%
2
4.76%
0
0.00%
5
42
0
0.00%
0
0.00%
0
0.00%
6
42
0
0.00%
0
0.00%
0
0.00%
7
42
0
0.00%
0
0.00%
0
0.00%
8
40
0
0.00%
3
7.50%
0
0.00%
9
44
0
0.00%
1
2.27%
0
0.00%
10
37
0
0.00%
0
0.00%
0
0.00%
11
43
0
0.00%
0
0.00%
0
0.00%
12
44
0
0.00%
0
0.00%
0
0.00%
13
44
0
0.00%
1
2.27%
0
0.00%
14
42
0
0.00%
0
0.00%
0
0.00%
15
42
0
0.00%
2
4.76%
0
0.00%
16
44
0
0.00%
0
0.00%
0
0.00%
17
45
0
0.00%
0
0.00%
0
0.00%
18
44
0
0.00%
1
2.27%
0
0.00%
19
45
0
0.00%
0
0.00%
0
0.00%
20
42
0
0.00%
0
0.00%
0
0.00%
21
43
0
0.00%
0
0.00%
0
0.00%
22
37
0
0.00%
0
0.00%
0
0.00%
23
45
0
0.00%
3
6.67%
0
0.00%
24
42
0
0.00%
0
0.00%
0
0.00%
25
42
0
0.00%
0
0.00%
0
0.00%
26
42
0
0.00%
2
4.76%
0
0.00%
27
44
0
0.00%
0
0.00%
0
0.00%
28
40
0
0.00%
3
7.50%
0
0.00%
29
46
0
0.00%
3
6.52%
0
0.00%
30
44
0
0.00%
0
0.00%
0
0.00%
31
42
0
0.00%
2
4.76%
0
0.00%
32
42
0
0.00%
4
9.52%
0
0.00%
33
45
0
0.00%
0
0.00%
0
0.00%
34
44
0
0.00%
0
0.00%
0
0.00%
35
44
0
0.00%
1
2.27%
0
0.00%
36
43
0
0.00%
1
2.33%
0
0.00%
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25
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Sample Record
#
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
37
42
0
0.00%
0
0.00%
0
0.00%
38
44
0
0.00%
2
4.55%
0
0.00%
39
43
0
0.00%
1
2.33%
0
0.00%
40
45
0
0.00%
0
0.00%
0
0.00%
41
41
0
0.00%
0
0.00%
0
0.00%
42
36
0
0.00%
1
2.78%
0
0.00%
43
45
0
0.00%
0
0.00%
0
0.00%
44
42
0
0.00%
0
0.00%
0
0.00%
45
45
0
0.00%
0
0.00%
0
0.00%
46
43
0
0.00%
1
2.33%
0
0.00%
47
45
0
0.00%
0
0.00%
0
0.00%
48
44
0
0.00%
3
6.82%
0
0.00%
49
44
0
0.00%
0
0.00%
0
0.00%
50
44
0
0.00%
0
0.00%
0
0.00%
51
44
0
0.00%
0
0.00%
0
0.00%
52
42
0
0.00%
0
0.00%
0
0.00%
53
42
0
0.00%
4
9.52%
0
0.00%
54
37
0
0.00%
0
0.00%
0
0.00%
55
42
0
0.00%
1
2.38%
0
0.00%
56
44
0
0.00%
1
2.27%
0
0.00%
57
42
0
0.00%
0
0.00%
0
0.00%
58
44
0
0.00%
1
2.27%
0
0.00%
59
44
0
0.00%
0
0.00%
0
0.00%
60
42
0
0.00%
0
0.00%
0
0.00%
61
42
0
0.00%
2
4.76%
0
0.00%
62
44
0
0.00%
0
0.00%
0
0.00%
63
44
0
0.00%
0
0.00%
0
0.00%
64
36
0
0.00%
5
13.89%
0
0.00%
65
43
0
0.00%
0
0.00%
0
0.00%
66
45
0
0.00%
0
0.00%
0
0.00%
67
44
0
0.00%
4
9.09%
0
0.00%
68
43
0
0.00%
3
6.98%
0
0.00%
69
44
0
0.00%
0
0.00%
0
0.00%
70
45
0
0.00%
0
0.00%
0
0.00%
71
42
0
0.00%
0
0.00%
0
0.00%
72
44
0
0.00%
0
0.00%
0
0.00%
73
44
0
0.00%
0
0.00%
0
0.00%
74
42
0
0.00%
2
4.76%
0
0.00%
75
44
0
0.00%
0
0.00%
0
0.00%
76
42
0
0.00%
0
0.00%
0
0.00%
77
42
0
0.00%
1
2.38%
0
0.00%
20-P-0026
26
-------
#
~78~
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
42
0.00%
2.38%
0.00%
44
0.00%
2.27%
0.00%
45
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
4.55%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
43
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
9.09%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
2.27%
0.00%
36
0.00%
2.78%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
2.27%
0.00%
44
0.00%
2.27%
0.00%
43
2.33%
2.33%
2.33%
44
0.00%
0.00%
0.00%
45
0.00%
2.22%
0.00%
43
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
41
2.44%
2.44%
2.44%
44
0.00%
2.27%
0.00%
44
0.00%
9.09%
0.00%
44
0.00%
9.09%
0.00%
44
0.00%
6.82%
0.00%
27
-------
#
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
44
0.00%
2.27%
0.00%
43
0.00%
2.33%
0.00%
44
0.00%
0.00%
0.00%
43
0.00%
2.33%
0.00%
42
0.00%
2.38%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
4.76%
0.00%
44
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
44
0.00%
9.09%
0.00%
40
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
45
0.00%
4.44%
0.00%
40
0.00%
5.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
4.76%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
4.76%
0.00%
43
0.00%
2.33%
0.00%
44
0.00%
0.00%
0.00%
28
-------
#
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
44
0.00%
9.09%
0.00%
40
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
40
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
44
0.00%
9.09%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
40
0.00%
5.00%
0.00%
43
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
43
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
45
0.00%
4.44%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
43
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
40
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
40
0.00%
5.00%
0.00%
29
-------
#
20?
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
45
0.00%
8.89%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
2.27%
0.00%
40
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
44
0.00%
11.36%
0.00%
45
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
44
0.00%
4.55%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
45
0.00%
2.22%
0.00%
42
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
45
0.00%
2.22%
0.00%
44
0.00%
6.82%
0.00%
44
0.00%
9.09%
0.00%
37
0.00%
0.00%
0.00%
41
0.00%
2.44%
0.00%
42
0.00%
0.00%
0.00%
45
0.00%
6.67%
0.00%
43
0.00%
4.65%
0.00%
42
0.00%
2.38%
0.00%
44
0.00%
0.00%
0.00%
45
0.00%
13.33%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
41
0.00%
0.00%
0.00%
41
0.00%
0.00%
0.00%
44
0.00%
4.55%
0.00%
44
0.00%
9.09%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
45
0.00%
0.00%
0.00%
30
-------
#
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
44
0.00%
2.27%
0.00%
44
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
15.91%
0.00%
42
0.00%
0.00%
0.00%
40
0.00%
2.50%
0.00%
42
0.00%
0.00%
0.00%
44
0.00%
0.00%
0.00%
42
0.00%
2.38%
0.00%
40
0.00%
0.00%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
2.70%
18.92%
2.70%
37
0.00%
5.41%
0.00%
41
12.20%
11
26.83%
12.20%
37
0.00%
8.11%
0.00%
37
0.00%
16.22%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
8.11%
0.00%
41
9.76%
21.95%
9.76%
37
0.00%
8.11%
0.00%
37
0.00%
5.41%
0.00%
38
0.00%
13.16%
0.00%
37
0.00%
13.51%
0.00%
37
0.00%
16.22%
0.00%
37
2.70%
13.51%
2.70%
37
0.00%
16.22%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
10.81%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
2.70%
18.92%
2.70%
41
9.76%
19.51%
9.76%
37
0.00%
10.81%
0.00%
41
12.20%
19.51%
12.20%
37
0.00%
8.11%
0.00%
35
0.00%
11.43%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
13.51%
0.00%
31
-------
#
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
38
33
86.84%
33
86.84%
33
86.84%
41
9.76%
17.07%
9.76%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
8.11%
0.00%
38
0.00%
18.42%
0.00%
37
0.00%
13.51%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
37
2.70%
18.92%
2.70%
41
9.76%
11
26.83%
9.76%
38
33
86.84%
34
89.47%
33
86.84%
35
0.00%
8.57%
0.00%
41
9.76%
17.07%
9.76%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
35
0.00%
17.14%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
10.81%
0.00%
37
0.00%
10.81%
0.00%
37
0.00%
10.81%
0.00%
38
33
86.84%
34
89.47%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
13.51%
0.00%
37
0.00%
10.81%
0.00%
37
0.00%
10.81%
0.00%
35
0.00%
17.14%
0.00%
37
2.70%
21.62%
0.00%
37
0.00%
18.92%
0.00%
37
33
89.19%
33
89.19%
33
89.19%
37
0.00%
13.51%
0.00%
38
33
86.84%
33
86.84%
33
86.84%
37
0.00%
21.62%
0.00%
37
0.00%
8.11%
0.00%
37
5.41%
21.62%
5.41%
37
0.00%
13.51%
0.00%
39
10.26%
20.51%
10.26%
38
0.00%
7.89%
0.00%
32
-------
Sample Record
#
Total # of
Data
Elements
# Incomplete
# Inaccurate
# Untimely
324
38
33
86.84%
33
86.84%
33
86.84%
325
37
33
89.19%
33
89.19%
33
89.19%
326
38
33
86.84%
34
89.47%
33
86.84%
327
37
0
0.00%
7
18.92%
0
0.00%
328
37
0
0.00%
6
16.22%
0
0.00%
329
37
0
0.00%
5
13.51%
0
0.00%
330
35
0
0.00%
5
14.29%
0
0.00%
331
35
0
0.00%
5
14.29%
0
0.00%
332
35
0
0.00%
8
22.86%
0
0.00%
Total Errors
936
1369
933
Sum of Error
Rates
2461.09%
3560.96%
2453.84%
/Number of
Samples
332
332
332
Projected Error
Rate
7.41%
10.73%
7.39%
Source: EPA OIG data analysis using DATA Act Audit Guide.
20-P-0026
33
-------
Appendix D
EPA's Results for the Data Elements
Accuracy (A), Completeness (C), Timeliness (T)
Number of Errors
in Samples
Total
Applicable
Samples
Projected
Error Rate
Data
Element
No.
Data Element Name
A
C
T
A
C
T
20
Catalog of Federal Domestic Assistance (CFDA) Title1
42
27
27
80
53%
34%
34%
13
Amount of Award1
30
27
27
80
38%
34%
34%
12
Non-Federal Funding Amount1
30
29
28
80
38%
36%
35%
37
Business Types1
28
27
27
80
35%
34%
34%
35
Record Type1
27
27
27
80
34%
34%
34%
19
Catalog of Federal Domestic Assistance (CFDA) Number1
27
27
27
80
34%
34%
34%
48
Awarding Office Name
82
27
27
332
25%
8%
8%
49
Awarding Office Code
82
27
27
332
25%
8%
8%
5
Legal Entity Address
71
27
27
332
21%
8%
8%
30
Primary Place of Performance Address
69
28
27
325
21%
9%
8%
16
Award Type
66
27
27
332
20%
8%
8%
4
Ultimate Parent Legal Entity Name
58
34
34
325
18%
10%
10%
31
Primary Place of Performance Congressional District
56
28
27
324
17%
9%
8%
6
Legal Entity Congressional District
47
27
27
331
14%
8%
8%
34
Award ID Number (PIID/FAIN) [Files D1/D2]
37
27
27
332
11%
8%
8%
2
Awardee/Recipient Unique Identifier
36
27
27
332
11%
8%
8%
36
Action Type
30
29
29
291
10%
10%
10%
23
Award Modification / Amendment Number
27
27
27
281
10%
10%
10%
1
Awardee/Recipient Legal Entity Name
34
27
27
332
10%
8%
8%
3
Ultimate Parent Unique Identifier
34
27
27
325
10%
8%
8%
27
Period of Performance Current End Date
34
27
27
327
10%
8%
8%
22
Award Description
29
27
27
332
9%
8%
8%
32
Primary Place of Performance Country Code
28
27
27
325
9%
8%
8%
7
Legal Entity Country Code
27
27
27
332
8%
8%
8%
8
Legal Entity Country Name
28
27
27
332
8%
8%
8%
11
Federal Action Obligation
27
27
27
332
8%
8%
8%
25
Action Date
28
27
27
332
8%
8%
8%
20-P-0026
34
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26
Period of Performance Start Date
27
27
27
332
8%
8%
8%
33
Primary Place of Performance Country Name
27
27
27
325
8%
8%
8%
44
Awarding Agency Name
27
27
27
332
8%
8%
8%
45
Awarding Agency Code
27
27
27
332
8%
8%
8%
46
Awarding Sub Tier Agency Name
27
27
27
332
8%
8%
8%
47
Awarding Sub Tier Agency Code
27
27
27
332
8%
8%
8%
24
Parent Award ID Number [File CI2
10
0
0
160
6%
0%
0%
24
Parent Award ID Number [File D112
10
0
0
160
6%
0%
0%
56
Program Activity [File CI
4
0
0
76
5%
0%
0%
42
Funding Office Name
11
8
8
260
4%
3%
3%
43
Funding Office Code
11
8
8
260
4%
3%
3%
38
Funding Agency Name
8
8
8
260
3%
3%
3%
39
Funding Agency Code
8
8
8
260
3%
3%
3%
34
Award ID Number (PIID/FAIN) [File CI
10
0
0
332
3%
0%
0%
14
Current Total Value of Award 2
5
0
0
245
2%
0%
0%
15
Potential Total Value of Award 2
4
0
0
252
2%
0%
0%
28
Period of Performance Potential End Date 2
5
0
0
245
2%
0%
0%
17
North American Industry Classification System (NAICS) Code 2
3
0
0
252
1%
0%
0%
18
NAICS Description 2
3
0
0
252
1%
0%
0%
53
Obligation [File CI
1
0
0
332
0%
0%
0%
29
Ordering Period End Date 2
0
0
0
7
0%
0%
0%
40
Funding Sub Tier Agency Name
0
0
0
252
0%
0%
0%
41
Funding Sub Tier Agency Code
0
0
0
252
0%
0%
0%
50
Object Class [File CI
0
0
0
332
0%
0%
0%
51
Appropriations Account [File CI
0
0
0
332
0%
0%
0%
Source: OIG sample analysis.
'Applicable only to FAIN (grant) samples.
2Applicable only to PUD (contract) samples.
Additional Notes:
EPA's results listed in descending order by accuracy error rate percentage.
The projected error rate is calculated by number of errors of each data element divided by the number of applicable contract and grant
samples. These error rates include the 27 samples with missing File D2 information. Therefore, 27 samples have errors marked for
completeness, accuracy and timeliness. For applicable File D2 elements, 27 out of 332 samples account for an 8 percent error rate.
For data elements that just relate to the 80 grant samples, the 27 samples missing File D2 information account for 34 percent.
Funding Agency Name, Funding Agency Code, Funding Office Name and Funding Office Code (Funding Office fields) are required for grants
that were new as of October 1, 2018. Eight grant samples fit this criterion, and they were all blank.
20-P-0026
35
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Appendix E
Analysis of the Accuracy of Dollar-Value-Related Data Elements
Our testing included tests of certain dollar-value-related data elements, such as Federal Action Obligation, Current Total Value of
Award, Potential Total Value of Award and Obligation. The table below shows the results of the accuracy of the data elements related
to dollar value. These dollar-value-related data elements cannot be projected to the DATA Act submission population.
Accuracy of Dollar-Value-Related Data Elements
piid /
FAIN
Data Element
Accurate
Not
Accurate
Not
Applicable
Total
Tested
Error
Rate
Absolute Value of
Errors
PIID
DE 11
Federal Action Obligation
252
0
0
252
0.0%
$0
PIID
DE 14
Current Total Value of
Award
240
5
7
245
2.0%
$562,538
PIID
DE 15
Potential Total Value of
Award
248
4
0
252
1.6%
$2,980,178
PIID
DE 53
Obligation
251
1
0
252
0.4%
$73,763
FAIN
DE 11
Federal Action Obligation
59
21
0
80
26.3%
$5,714,763
FAIN
DE 12
Non-Federal Funding
Amount
69
11
0
80
13.8%
$2,609,632
FAIN
DE 13
Amount of Award
55
25
0
80
31.3%
$8,324,395
FAIN
DE 53
Obligation
80
0
0
80
0.0%
$0
Source: OIG sample analysis.
20-P-0026
36
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Appendix F
Agency Response to Draft Report
121Ej
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D C. 20460
NOV 0 ? 2019
MEMORANDUM
OFFICE OF
CHIEF FINANCIAL OFFICER
SUBJECT: Response to Office of Inspector General Draft Audit Report, Project No. OA&E-FY19-
0124, "EPA's Fiscal Year 2019 First Quarter Compliance with the Digital Accountability
/'and Transparency Act of 2014, " dated November 1, 2019
FROM: ( nit Chief Finnnrial OflWr
^Office of the Chief Financial Officer
TO: Paul C. Curtis, Director
Financial Directorate
Office of Inspector General
Thank you for the opportunity to respond to the issues and recommendations presented in the subject
draft audit report. The draft report includes two recommendations, which are directed to the Office of
the Chief Financial Officer and the Office of Mission Support. The agency concurs with both
recommendations.
AGENC Y RESPONSE TO REPORT RECOMMENDATIONS:
No.
Recommendation
Assigned
to
Agency Explanation/Response
Completion
Date
1
Develop and document standard
operating policies and procedures
specific to the completeness,
accuracy, timeliness and quality of
the EPA's DATA Act reporting
(consistent with DATA Act
requirements). These procedures
should also define roles and
responsibilities for performing
validation procedures.
OCFO and
OMS
In FY 2020. the OCFO and the
OMS will develop Standard
Operating Procedures for the
DATA Act submission reviews.
These SOPs will include best
practices to correct quickly
common DATA Act
inconsistencies, define roles and
responsibilities, and support sound
business processes for the
submission review and approvals.
Many of these elements were
addressed in the agency's Data
Quality Plan: however, the plan
was not finalized in time for
review during this audit. The
agency will prepare the SOPs by
September 30, 2020.
September
30, 2020
Internet Address (URL) http //www epa gov
Recycled/Recyclable Printed with Vegetable Oil Based Inks on 100% Postconsumer. Process Chlorine Free Recycled Paper
20-P-0026
37
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Continue to coordinate with
Treasury to eliminate inconsistent
use of OMB and Treasury-
established data standards.
OCFO and
OMS
The OCFO and the OMS will
continue to review. monitor, and
implement changes to the DATA
Act Information Model Schema.
The DAIMS is the Department of
(he Treasury's guidance document
as to the standards for every data
element required and optional in
the DATA Act submissions. In FY
2020, the agency will re-evaluate
all of the current DAIMS data
elements and assess whether the
current processes are properly
capturing the associated data
elements. The agency will
complete this action by September
30, 2020.
September
30, 2020
CONTACT INFORMATION
If you have any questions regarding this response, please contact Andrew I eBlanc, Agency Audit
Follow Up Coordinator, on (202) 564-1761 or via email lcblanc.andrev\ j epa.jov.
cc: Chuck Sheehan
lid Shields
Kevin Christenscn
Carol Ferris
Paige Hanson
Charlie Dankert
Jeanne Conklin
Richard Gray
Donna Vizian
David Zeekman
Vaughn Noga
Wesley Carpenter
Kimberly Patrick
Michael Osinski
Denise Polk
I'am 1 .egare
Meshell Jones-Peeler
Eva Ripollone
Brian Webb
Ailecn Ateherson
Judi Doueette
Nikki Wood-Newton
Hbonic Smith
Andrew LeBlanc
20-P-0026
38
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Appendix G
Distribution
The Administrator
Assistant Deputy Administrator
Associate Deputy Administrator
Chief of Staff
Deputy Chief of Staff
Chief Financial Officer
Associate Chief Financial Officer
Associate Chief Financial Officer for Policy
Agency Follow-Up Coordinators, Office of the Administrator
General Counsel
Associate Administrator for Congressional and Intergovernmental Relations
Associate Administrator for Public Affairs
Director, Office of Continuous Improvement, Office of the Administrator
Associate Administrator for Policy, Office of the Administrator
Director, Office of Budget, Office of the Chief Financial Officer
Controller, Office of the Controller, Office of the Chief Financial Officer
Deputy Controller, Office of the Controller, Office of the Chief Financial Officer
Director, Office of Planning, Analysis and Accountability, Office of the Chief Financial Officer
Director, Office of Resource and Information Management, Office of the Chief Financial Officer
Director, Office of Technology Solutions, Office of the Chief Financial Officer
Principal Deputy General Counsel
Principal Deputy Assistant Administrator, Office of Mission Support
Associate Deputy Assistant Administrator, Office of Mission Support
Director, Administrative IT Staff, Office of Mission Support
Director, Office of Resources and Business Operations, Office of Mission Support
Director, Information Security and Management Staff, Office of Mission Support
Deputy Assistant Administrator for Administration and Resources Management, Office of
Mission Support
Director, Office of Acquisition Solutions, Office of Mission Support
Director, Office of Administration, Office of Mission Support
Director, Office of Grants and Debarment, Office of Mission Support
Senior Debarring Official, Office of Grants and Debarment, Office of Mission Support
Senior Associate Director for Grants Competition, Office of Grants and Debarment, Office of
Mission Support
Director, Office of Human Resources, Office of Mission Support
Audit Follow-Up Coordinator, Office of the Administrator
Audit Follow-Up Coordinator, Office of Mission Support
Audit Follow-Up Coordinator, Office of Budget, Office of the Chief Financial Officer
Audit Follow-Up Coordinator, Office of the Controller, Office of the Chief Financial Officer
Audit Follow-Up Coordinator, Office of Technology Solutions, Office of the Chief Financial Officer
Audit Follow-Up Coordinator, Office of Acquisition Solutions, Office of Mission Support
Audit Follow-Up Coordinator, Office of Grants and Debarment, Office of Mission Support
20-P-0026
39
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