Why This Forum Was Convened 

Oversight and law enforcement 

agencies play an important role in 

eliminating fraud, waste, and abuse. 

Sharing data, knowledge, and analytic 

tools can assist government agencies 

in this effort. However, while there is a 

tremendous amount of information the 

government can use in preventing and 

detecting fraud, waste, and abuse, 

using and leveraging these data can 

be challenging.

In January 2013, GAO, the Council 

of the Inspectors General on Integrity 

and Efficiency, and the Recovery 

Accountability and Transparency 

Board convened a forum with the 

purpose of exploring ways in which 

oversight and law enforcement 

agencies use data analytics to assist 

in the prevention and detection of 

fraud, waste, and abuse, as well as 

identifying the most-significant 

challenges to realizing the potential 

of data analytics and actions that 

the government can take to address 

these challenges.

This report summarizes the key 

themes that emerged from the 

discussion in the forum. Specifically, 

the report discusses the challenges 

and opportunities in (1) accessing and 

using data and (2) sharing data. In

addition, participants identified next 

steps to address these challenges 

and capitalize on opportunities.

Forum participants included 

representatives from federal, state, 

and local government agencies as 

well as the private sector.

View GAO-13-680SP. For more information, 

contact Steve M. Lord at (202) 512-4379 or 

lords@gao.gov. 

What Participants Said

Participants identified a range of challenges and opportunities associated with data 

analytics—which involve a variety of techniques to analyze and interpret data to 

facilitate decision making—as discussed below. 

Challenges and opportunities in accessing and using available data

Challenges participants cited include, among other issues, oversight and law 

enforcement entities not always being aware of all the data that may be available 

to assist them in their duties, and a lack of incentives for program offices to develop 

information-technology systems to support data analysis by oversight and law 

enforcement entities. Participants also noted other issues related to managing and using 

data such as challenges in developing a strategy to prioritize limited resources and the 

difficulty oversight entities face in measuring the success of fraud-prevention efforts.

Participants also identified opportunities to enhance data-analytics efforts, such as 

consolidating data and analytics operations in one location to increase efficiencies 

by enabling the pooling of resources as well as accessing and sharing of the data. 

Participants further identified strategies to garner organizational support for 

data-analytics programs, such as short-turnaround projects that produce quick, 

valuable successes to highlight the value of data analytics. 

Challenges and opportunities in sharing data

Challenges participants cited include, among other issues, certain statutory requirements 

that place procedural hurdles on agencies wishing to perform data matching to detect 

fraud, waste, and abuse, and technical obstacles—such as the lack of uniform data 

standards across agencies—which make it more difficult for oversight and law 

enforcement entities to share available data. Participants also noted challenges in 

sharing data across federal, state, and local government agencies due to a variety of 

factors including actual and perceived legislative barriers. 

Participants also discussed opportunities that could be realized if the government utilized 

and shared interoperable, open-source analytical tools and techniques, which could 

lessen the challenge of developing licensing agreements for proprietary software tools, 

be utilized at a low cost, and be tailored to meet the needs of individual agencies.

Next steps

Participants identified several next steps that the three sponsoring organizations agreed 

to implement, including: compiling a consolidated directory of data sources to increase 

awareness; compiling a library of available open-source data analytics, modules, and 

tools; developing an ongoing community of practice focused on data-sharing challenges; 

and examining the existing statutory framework to determine whether changes related 

to challenges and barriers for data analytics would be useful to oversight and law 

enforcement agencies in carrying out their missions.

Government Accountability Office

Council of the Inspectors General on Integrity and Efficiency

Recovery Accountability and Transparency Board

DATA ANALYTICS

FOR OVERSIGHT & LAW ENFORCEMENT

CONVENED BY THE

HIGHLIGHTS OF A FORUM
Page i GAO-13-680SP Data Analytics Forum

Letter 1 

Foreword from the Forum Hosts 1

Participants Identified Challenges Related to Accessing and Using 

Data to Identify Fraud, Waste, and Abuse and Discussed 

Opportunities to Enhance Analytics Efforts 3

Forum Participants Identified Several Challenges and 

Opportunities Related to Data Sharing across Government 10

Forum Participants Identified Next Steps in Addressing Challenges 17

Appendix I Forum Agenda 20

Appendix II Forum Participants 21

Appendix III Related Products and Image Sources 24

Appendix IV Contacts and Staff Acknowledgments 27

Contents 
Page ii GAO-13-680SP Data Analytics Forum

Abbreviations

CIGIE Council of the Inspectors General on Integrity 

and Efficiency

CMS Centers for Medicare & Medicaid Services

Computer Matching Act Computer Matching and Privacy Protection Act

DMF Death Master File

FISMA Federal Information Security Management Act 

of 2002

FOIA Freedom of Information Act

FPS Fraud Prevention System

HIPAA Health Insurance Portability and Accountability 

Act

HIV human immunodeficiency virus

IG Inspector General

IT information technology

Recovery Board Recovery Accountability and Transparency 

Board

ROC Recovery Operations Center

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. The published product may be reproduced and distributed in its entirety 

without further permission from GAO. However, because this work may contain 

copyrighted images or other material, permission from the copyright holder may be 

necessary if you wish to reproduce this material separately.
Page 1 GAO-13-680SP Data Analytics Forum

Recent financial and economic conditions have prompted greater scrutiny 

of government spending, highlighting the important role oversight and law 

enforcement agencies play in identifying and eliminating fraud, waste, 

and abuse. A number of government oversight and law enforcement 

agencies are using data analytics—which involve a variety of techniques 

to analyze and interpret data—to help identify and reduce fraud, waste, 

and abuse. For example, predictive analytic technologies can identify 

fraud and errors before payments are made, while others, such as datamining and data-matching techniques, can identify fraud or improper 

payments that have already been awarded, thus assisting agencies in 

recovering these dollars. However, despite these efforts, the path to 

capitalizing on the potential of data analytics is not a clear one. While 

there is a tremendous amount of information that the government can 

use, identifying what data are available and then finding ways to analyze 

and use the data effectively are critical steps to moving forward.

In January 2013, our organizations convened a forum to explore these 

issues. The forum offered a series of facilitated discussion sessions (see 

app. I for the agenda). During the first session, participants discussed 

data sources that are useful for preventing and detecting fraud. During 

the second session, they discussed opportunities and challenges related 

to accessing and sharing data. The group then heard from private-sector 

participants about data-analytics tools—including open-source tools—that 

are available to assist in fraud prevention and detection. The group also 

discussed examples of how data are being used for oversight, and the 

challenges related to managing resources and prioritizing work. 

Participants completed the day with a session discussing how oversight 

and law enforcement agencies might address the challenges identified 

throughout the forum, and identified several next steps that our 

organizations agreed to implement. This report summarizes key themes 

that emerged during the forum. Specifically, the report discusses the 

challenges and opportunities in (1) accessing and using data and (2) 

sharing data. In addition, it describes the next steps to address these 

challenges and capitalize on opportunities.

Forum participants were selected to reflect a range of viewpoints and 

included representatives from federal, state, and local government 

agencies as well as the private sector. The forum was structured so that 

participants could comment on issues openly, although not all participants 

commented on all topics. This report—jointly developed by our 

organizations—summarizes the ideas and themes that emerged at the 

forum and the collective discussion of participants at the sessions. The 

summary does not necessarily represent the views of any individual 

Foreword from the 

Forum Hosts
Page 2 GAO-13-680SP Data Analytics Forum

participant or the organizations that these participants represent, 

including GAO, the Council of the Inspectors General on Integrity and 

Efficiency, and the Recovery Accountability and Transparency Board.

We would like to thank all the participants of this forum for taking the time 

to share their knowledge and insights on improving data analytics for 

detecting and preventing fraud, waste, and abuse. We look forward to 

working with them and others moving forward on the issues identified.

Gene L. Dodaro

Comptroller General

of the United States

Phyllis K. Fong

Inspector General

U.S. Department of Agriculture

Chairperson

Council of the Inspectors General on Integrity and Efficiency

Kathleen S. Tighe

Inspector General

U.S. Department of Education

Chair

Recovery Accountability and Transparency Board

July 15, 2013
Page 3 GAO-13-680SP Data Analytics Forum

Despite the vast amount of data available, participants stated that 

oversight and law enforcement entities were not always aware of all the 

data that may be available to assist them in their duties, which limits the 

abilities of oversight and law enforcement entities to leverage such data 

to better carry out their work. Prior to the forum, GAO, the Council of the 

Inspectors General on Integrity and Efficiency (CIGIE), and the Recovery 

Accountability and Transparency Board (Recovery Board) worked 

together to ask federal oversight and law enforcement entities to submit 

basic information on data sources they use in their efforts to identify 

fraud, waste, and abuse. GAO aggregated the responses and shared a 

list with participants during the forum. Participants agreed that the list was 

useful in helping them to gain additional awareness of available data, and 

suggested that additional information sharing about the data sources 

could further enhance their abilities to effectively carry out their missions.

Participants also cited an interest in finding ways to identify sources of 

information on known offenders, or “bad actors,” that agencies identified 

as having taken advantage of the government in an abusive or fraudulent 

Participants Identified 

Challenges Related to 

Accessing and Using 

Data to Identify 

Fraud, Waste, and 

Abuse and Discussed 

Opportunities to 

Enhance Analytics 

Efforts

Participants Identified 

Several Challenges Related 

to Accessing and Using 

Data

Oversight Entities Were Not 

Always Aware of the Data 

Available to Identify Fraud, 

Waste, and Abuse 
Page 4 GAO-13-680SP Data Analytics Forum

manner.1 Participants generally agreed that the federal government could 

do more to share information about known offenders, and participants 

from both the government and the private sector recommended that 

efforts be made to increase awareness and share information on these 

individuals to advance oversight and law enforcement efforts. Participants 

noted that there is a wide array of data that exist about such individuals, 

such as individual Inspector General (IG) databases, public court records, 

and data on contractors and grant recipients; however, the information is 

not always collected or shared in a systematic way.

Participants said that their government program offices lack incentives to 

develop information-technology (IT) systems that would be useful for 

oversight efforts. Participants reported that, because of the lack of 

incentives, there is a general reluctance to develop systems that would be 

useful in identifying fraud, waste, and abuse.

Participants noted that while all agencies are concerned about fraud, 

waste, and abuse, the core mission of any program office is to administer 

the programs. As a consequence, participants stated that program offices 

generally do not design IT systems with oversight in mind. For example, 

participants explained that program offices responsible for providing 

benefits to members of the public are often focused on doing so as 

efficiently as possible and design their IT systems to assist in meeting this 

goal. In contrast, oversight entities focus on reviewing whether policies 

are being enforced regarding beneficiary eligibility and identifying areas 

where programs may be vulnerable to fraud, waste, or abuse, according 

to forum participants. Participants stated that because of the difference in 

missions, oversight entities may use program-office data in a way that is 

different from how it was originally intended, which can create challenges. 

Forum participants noted that oversight entities could better incentivize 

program offices to design systems conducive to oversight. In particular, 

one participant noted that oversight entities may have better success in 

persuading program offices to design systems with capabilities that go 

beyond their primary program mission if they can demonstrate how the 

added capabilities will be beneficial.

 

1

For example, the Office of the Inspector General of the U.S. Department of Health and 

Human Services maintains a List of Excluded Individuals and Entities. Entries on this list 

are excluded from participation in all federal health care programs because they have 

been convicted of offenses such as patient abuse, health care–related financial 

misconduct, and other reasons.

Program Offices Lack 

Incentives to Design 

Information-Technology 

Systems to Facilitate the 

Identification of Fraud, Waste, 

and Abuse
Page 5 GAO-13-680SP Data Analytics Forum

Forum participants identified several challenges related to managing and 

using data in their organizations and measuring the success of their 

efforts, as discussed below.

• The amount of data available can often be overwhelming for oversight 

and law enforcement agencies, and developing a strategy to prioritize 

limited resources is difficult. One participant stated that his office is 

overwhelmed with the number of investigative leads generated from 

its data-analytics activities. Participants cited various methods to 

prioritize work. For example, one participant stated that his office uses 

analytical methods to prioritize cases on the basis of the potential for 

a large recovery. Another participant stated that the fraud- prevention 

system her office uses incorporates a continuous feedback loop that 

refines the way the office prioritizes cases. In addition, senior 

management takes into account the amount of time that it will take to 

investigate and resolve cases when deciding where to allocate 

resources.

• Participants cited various legal implications of owning and maintaining 

data. Many participants said that efficiently allocating resources—and 

minimizing legal implications of owning and maintaining data—is key, 

and stressed the importance of a planned approach to acquiring the 

data. Agencies must consider many legal issues—including Freedom 

of Information Act disclosure requirements,2 Federal Information 

Security Management Act of 2002 (FISMA) information-security 

requirements,3 and protocols for records retention when obtaining 

data. One participant noted that once a government agency obtains 

data—even if they are acquired from another entity—the receiving 

agency may have a legal requirement to properly safeguard them and 

to follow applicable record-retention requirements. This participant 

recommended partnering with legal experts early on when acquiring 

data to better understand the different legal constructs and any 

limitations in how the data can be used. Participants encouraged a 

selective approach to acquiring data, noting that—given resource 

constraints—it is important not to try to gain access to all data, but to 

have a targeted approach in meeting specific objectives.

 

2

5 U.S.C. § 552.

3

44 U.S.C. §§ 3541-3549.

Challenges Exist in Managing 

and Using Data and Measuring 

the Results of Fraud-Prevention 

Efforts
Page 6 GAO-13-680SP Data Analytics Forum

• Measuring the success of analytics programs—especially in efforts to 

prevent fraud, waste, and abuse—is difficult. Some participants 

explained that it is difficult to measure the effect of prevention efforts, 

that is, savings associated with funds that were never stolen or 

wasted as a result of successful data-analytics and fraud-prevention 

efforts. Some participants said that their offices are seeking to 

measure the success of their analytics programs by analyzing trends 

over time. For example, one participant stated that his office used to 

measure results by how much money was recovered and how many 

suspects were caught, but now, to measure prevention, the office 

looks at whether there has been a reduction in a particular type of 

fraudulent activity. 

Participants highlighted several opportunities to apply lessons learned to 

develop and enhance existing data-analytics capabilities.

• Knowledgeable and skilled staff are essential. Forum participants 

stressed that having data and analytics tools are not enough; rather, it 

is crucial to also have talented staff onboard to perform data analysis 

and identify high-quality investigative leads and areas that are the 

greatest risk to the government. Knowledgeable and skilled analysts 

have the ability to see information well, understand how to go deeper 

into the data, and find complementary data through other sources. 

Participants commented that computers are no substitute for humans 

and there is no “find fraud” button; a computer can identify indicators 

but cannot perform the same kind of evaluation that a human can. For 

example, analysts can look at the results of a computer analysis and 

determine whether the pattern can be explained away or if there are 

signs of fraudulent activity. Another participant emphasized the 

persistent challenge in finding the optimal balance between people 

and computers.

• Consolidating data and analytics operations into one location can 

enhance return on investment. Forum participants said that 

consolidating disparate databases can help increase efficiencies by 

enabling the pooling of resources as well as accessing and sharing of 

the data. For example:

Participants Identified 

Opportunities to Enhance 

Data-Analytics Efforts

Opportunities Exist to Help 

Develop and Enhance DataAnalytics Capabilities 
Page 7 GAO-13-680SP Data Analytics Forum

• One participant said that instead of many groups doing smallscale analytics in a limited way, creating one larger group could 

offer greater synergies and access to more resources. For 

example, a larger group would have enough resources to 

purchase data tools and systems that would have been out of 

reach for smaller groups.

• Another participant stated that sharing databases could create 

challenges without some level of centralization, where there is a 

responsible party for retaining and maintaining the database. 

Another participant explained that identifying an agency to serve 

as the custodian of those data would help ensure proper 

safeguards are in place to protect privacy.

• Using predictive analytics to identify fraudulent claims before they are 

paid can be preferable. Participants believed that benefits can be 

gained from moving from a “pay-and-chase” model—where agencies 

attempt to recover fraudulent claims that have been paid—to using 

predictive analytics to identify fraudulent claims before they are paid. 

Participants noted that recovering money lost due to fraud was 

difficult, and underscored the need to amplify preventative efforts. 

Some participants cited the Centers for Medicare & Medicaid 

Services’ (CMS) Fraud Prevention System (FPS) as an example of a 

data-analytics system designed to detect and prevent fraud. This 

system—developed as a result of a statutory mandate—uses a 

combination of analytic models simultaneously to analyze billions of 

claims data to identify questionable claims for further analysis and 

investigation, one participant stated. Specifically, claims deemed 

suspect are referred for further program or law enforcement review, or 

both, and determinations are then fed back into the system. The 

participant said that this feedback loop is designed to allow for 

constant learning and for the predictive model to be continuously 

refined to detect fraudulent claims. One participant said that having a 

comprehensive team of experts is a crucial component of the 

program. For example, FPS teams are made up of analysts, 

statisticians, attorneys, medical professionals, and law enforcement 

personnel who meet together to solve problems collaboratively.

The Recovery Board’s participants stated that the positive impact of the 

Recovery Operations Center (ROC) is directly attributable to the 

implementation of the types of opportunities and lessons learned that 

participants highlighted, such as consolidating government data in one 

location supported by state-of-the-art analytic tools and experienced 

highly-skilled staff who can leverage tools for data analysis. Recovery 
Page 8 GAO-13-680SP Data Analytics Forum

board participants noted that the ROC is uniquely positioned to serve the 

law enforcement community with this type of analytical support.

One of the themes that emerged during the forum was the importance of 

building support within an organization for using data analytics to identify 

fraud, waste, and abuse. Participants discussed various strategies for 

starting a data-analytics operation and identified the following factors as 

being key to successfully obtaining institutional support:

• Obtaining cultural acceptance across the entire oversight or law 

enforcement entity or both is important when building a data-analytics 

team. 

• One participant shared his experience in setting up a dataanalytics operation, stating that, in some ways, getting the dataanalytics technology in place is the easy part; getting the 

technology accepted into the organization’s culture was more 

challenging. According to this participant, to help ensure that the 

data-analytics technology is accepted into the culture, 

relationships across the organization need to be strengthened. 

Gaining cultural acceptance can be a challenge because it can be 

difficult to convince those who do not normally work with data to 

integrate data analytics into their work, another participant noted.

• Another participant who has experience setting up a dataanalytics system stressed the importance of having seniormanagement involvement and support.

• Demonstrating “early victories” and a quick return on investment can 

help highlight the value of data analytics. 

• One participant found that although calculating return on 

investment can be challenging, it is still important for agencies to 

demonstrate the effect of their work when asking for funding and 

other resources.

• Another participant said that targeting short-turnaround projects 

that produce quick, valuable successes helped demonstrate the 

value of the data-analytics group and garnered greater support for 

subsequent efforts. The participant’s office found that delivering 

early, impactful victories and results helped change an agency’s 

culture to embrace data analytics.

Garnering Organizational 

Support in Using Data 

Analytics Is Key to Success 
Page 9 GAO-13-680SP Data Analytics Forum

• Another participant stated that part of the return on investment in 

data analytics is becoming a proactive organization in which 

investigators seek out fraud, waste, and abuse, rather than 

continuing to be a reactive organization that waits for requests 

from the outside.

• Tailoring information to the needs of the end users so that appropriate 

action can be taken is important. Several participants emphasized the 

importance of data visualization in showing the value of their work. 

For example:

• One participant found that in reporting the results of the data 

analytics to stakeholders, there needs to be a balance between 

substantive content and presentation; communicating strategies or 

plans through colorful graphics had been far more effective than 

through an inundation of spreadsheets.

• This participant found that combining high-quality data output with 

illustrative visuals has helped his data-analytics team gain 

acceptance.

• Another participant shared that she worked with end users to find 

out what information they need for their investigations. In 

response, instead of providing investigators raw data and risk 

scores, she provided tailored information that investigators need in 

order to do their work.

• Thoroughly vetting investigative leads generated through data 

analysis—to identify and remove false positives—can build credibility 

for analytics programs. One participant noted that, from his 

experience, if his office had referred cases for investigation and they 

turned out to be false positives—namely, improper identification of 

individuals or entities that were not engaged in fraud—future referrals 

would be ignored. He stressed the importance of developing a 

process to vet cases before making an investigative referral. He noted 

that one of his team’s recent analytical models identified 85,000 

individuals associated with potential fraudulent activities. However, 

sending such a large output to the investigators for follow-up activities 

would have resulted in numerous false positives and an eventual 

erosion of faith in the work of his team. He therefore created a team to 

evaluate the results to reduce the number of false positives by 

comparing the output from the data analytics to other data sources 

before referring cases to investigators.
Page 10 GAO-13-680SP Data Analytics Forum

Participants generally acknowledged the importance of the protections 

that privacy laws set forth; however, participants from the IG community 

and the Recovery Board noted concerns with computer matching 

requirements in the Privacy Act that they said were overly burdensome 

and hindered their ability to detect fraud, waste, and abuse.4 These 

participants stated that there must be a balance between safeguarding 

privacy and increasing the transparency of data. The Privacy Act is the 

principal law aimed at protecting personal privacy, with an underlying 

purpose to provide certain safeguards to individuals against invasion of 

privacy with respect to the collection and disclosure of personal 

information and to provide citizens with certain rights and a degree of 

fairness regarding the use of such information by the federal government. 

In doing so, the laws require federal agencies to establish rules and 

procedures for maintaining and protecting personal data in agency record 

systems. The Privacy Act has been amended over time to require

agencies to demonstrate the purpose and benefits of proposed computer 

matching programs and ensure due process and accountability. 

 

4

The Computer Matching and Privacy Protection Act (Computer Matching Act) was 

passed by Congress in 1988 as an amendment to the Privacy Act of 1974 (5 U.S.C. § 

552a) to provide safeguards regarding an agency’s use of certain records when 

performing certain computer matching programs. The Privacy Act of 1974 is the primary 

act that regulates the federal government’s use of personal information. The Privacy Act 

places limitations on agencies’ collection, disclosure, and use of personal information in 

systems of records.

Forum Participants 

Identified Several 

Challenges and 

Opportunities Related 

to Data Sharing 

across Government

Participants Identified 

Several Challenges Related 

to Data Sharing

Legal Constraints May Hinder 

Agencies’ Ability to Detect 

Fraud, Waste, and Abuse 
Page 11 GAO-13-680SP Data Analytics Forum

• Participants from the IG community and the Recovery Board raised 

concerns that the Computer Matching Act created impediments to 

government data collection and analytics activities to detect improper 

payments and fraud. Participants from the IG community and 

Recovery Board pointed to the multiple steps and processes required 

by the Computer Matching Act, which often take a long time before 

oversight entities can use agency data for matching purposes. For 

instance, these participants said that agencies have slow processes 

for evaluating potential data-sharing agreements and that there is no 

incentive for an agency to move more quickly in evaluating and 

approving these agreements. One forum participant stated that a 

recent computer matching agreement took approximately 2 years to 

get finalized. Some participants noted that the Computer Matching Act 

generally only applies to a Federal agency's computerized 

comparison of its lawfully collected data against data collected by 

another agency to identify program eligibility or regulatory compliance 

issues or to recover payments or delinquent debts. These participants 

believed it significant that the act does not govern the agency's ability 

to acquire this data and—since it only applies to electronic matching—

does not restrict, for example, a side-by-side comparison of two 

hardcopy lists of private information.

One forum participant expressed his belief that adoption by agencies 

of overly conservative interpretations of matching requirements may 

be the true barrier, and not the law itself. This participant suggested 

that conservative interpretations could be addressed by 

demonstrating to agencies the value of sharing data and providing 

them with incentives to identify ways to work within current law to do 

so. Other participants agreed that conservative or incorrect 

interpretations of existing legislation represent a barrier and that 

opportunities exist to address this issue by reaching agreement on a 

common understanding of the law.

• Some participants stated that certain provisions of the Computer 

Matching Act threaten IG independence. Under the Inspector General 

Act, IG offices are established as independent offices within their host 

agency, and participants from the IG community raised concerns that 

provisions of the Computer Matching Act threaten the principle of 

independence.5 They explained that the act requires, among other 

 

5

Inspector General Act of 1978, Pub. L. No. 95-452, §§ 2, 3 (Oct. 12, 1978). 5 U.S.C. App. 

3.
Page 12 GAO-13-680SP Data Analytics Forum

things, that IG offices obtain the approval of the agency’s Data 

Integrity Board in order to implement a computer matching 

agreement.6 Although the act includes each IG as a member of his or 

her host agency’s board, the majority of the board members are not 

officials from the IG office. Participants from the IG community 

expressed concerns that requiring these agency officials to approve 

an IG office–proposed data match could allow a board to prevent the 

match, or to impose undue restrictions or conditions on the match, 

thereby compromising the IG’s independent ability to determine the 

scope and methodology of the IG office’s audit or investigation. In 

addition, requiring approval from the Data Integrity Board provides 

other agency officials who are not on the board advance notice 

regarding the details of IG planned actions, which could impair 

sensitive or confidential work by the IG. Participants noted that there 

are provisions that allow a Data Integrity Board decision to be 

appealed to the Office of Management and Budget; however, they did 

not believe this was a remedy to the independence impairment.

• The IG community has sought a legislative exemption from the 

Computer Matching Act. IG community participants stated that they 

believed oversight and law enforcement entities should be exempt 

from Computer Matching Act requirements. Although recognizing the 

importance of Privacy Act protections, IG community participants 

commented that they believed the ability to perform data matching to 

identify fraud, waste, and abuse outweighs the privacy protection 

provided by the act. CIGIE has publicly supported a legislative 

exemption to allow IG offices greater ability to match data for 

oversight and law enforcement purposes. Several government 

participants noted the need to update existing requirements that were 

developed several decades ago to account for technology changes 

and current privacy concerns. Private-sector participants agreed that 

legislation has lagged behind technology, affecting how experts can 

utilize available data.

Participants said that federal and state government agencies are not 

working from a single set of data standards, which limits the ability of the 

participant’s agency to share and use data because of difficulty in 

integrating systems and interpreting varying definitions for data elements. 

 

6

Each federal agency must establish an internal Data Integrity Board to oversee and 

coordinate its data matching activities.

Data Standards Vary across the 

Government, Limiting the Ease 

of Sharing Data
Page 13 GAO-13-680SP Data Analytics Forum

One participant said that the lack of uniformity hinders her agency’s ability 

to quickly put data to use. Another said that the lack of standards leads to 

difficulties for the oversight community.

One participant highlighted a lack of standards and interoperability of 

state systems designed to administer federal programs as a particular 

challenge in conducting oversight. This participant explained that federal 

agencies require states to set up their own data systems to manage 

federally funded programs without offering a data standard guideline. 

Another participant cited Medicaid as an example of a federally supported 

program that could better define data requirements. Because of this, 

when federal oversight offices subsequently attempt to audit federally 

funded programs administered by states, federal auditors sometimes 

have difficulty interpreting the data because the states collect data based 

on their own individual data standards. One participant with experience 

working for a state agency said that the federal government could 

establish national criteria that the states could incorporate when 

developing systems, and that such systems could be reviewed by the 

funding agency to ensure consistency prior to implementation of the data 

system. Such an approach could ensure the development of a set of 

national data standards and would allow for the seamless sharing of 

information by all participating entities, according to the participant. 

Several participants believed the federal government is in the best 

position to set these standards since the impetus of these programs 

begins with the granting of federal funding.

Participants provided examples of uniform standards that could assist in 

improving interoperability of data and systems, which could better enable 

federal, state, and local governments to collaborate to share and use 

data. For example:

• Some participants said that a universal identification namingconvention standard for awards, or “universal award ID,” is one such 

uniform standard that could improve interoperability and help crossagency efforts.7 A unique government-wide universal award ID for all 

federal contracts, grants, and loans would make it much easier to 

track and reconcile funds awarded to recipients of federal funds, 

 

7

An award ID is the number that agencies assign to contracts, grants, and loans. There is 

currently no requirement that award IDs be standardized across the government.
Page 14 GAO-13-680SP Data Analytics Forum

according to one participant.

• One participant said that to make data sharing useful, “identity 

resolution” should be a key consideration in developing datasets. 

Identity resolution involves using common identifiers—such as Social 

Security Numbers or Employer Identification Numbers—across 

datasets to allow agencies to accurately identify individuals and 

entities, which is critical to matching records in datasets.

• Another participant said that a data dictionary ensures everyone is 

using the same definitions.

Participants said that program offices often lack incentives to share data 

they collect with the oversight community. For example, some forum 

participants said that agency program offices—which often are the 

collectors and owners of data—can feel threatened by oversight 

agencies. Multiple participants attributed reluctance in sharing to a lack of 

trust between program and oversight entities. One participant added that 

agencies invest a lot of resources into developing data and may feel their 

data are proprietary and, as a consequence, may not want to give it away 

for free. Participants offered ideas on better incentivizing program offices 

to share their data. For example, while asking program offices to become 

more auditable may not be a convincing argument, one participant noted 

that oversight entities could better seize opportunities to build bridges 

between program and oversight entities by treating program offices like 

useful resources and allies. Forum participants agreed that fostering a 

more collaborative relationship could incentivize program offices to build 

systems and provide data that are more useful for oversight operations.

Further, participants from the IG community raised questions about the 

extent to which they can share data analytics tools and the results of 

using those tools with agencies that would help agencies prevent fraud, 

waste, and abuse and yet still maintain independence. For example, one 

participant noted that the IG office shares certain information on effective 

ways to identify fraud with the agency it oversees in order for the agency 

to develop a process for preventing future similar fraudulent activities 

rather than trying to recover the payment after it has been made. The 

participant said that his office would like to share its analytics tools and 

the results therefrom; however, sharing such tools and information can 

create concerns about independence. For example, an IG’s sharing of 

such tools and information with the agency it oversees could raise 

questions about the IG’s independence under Government Auditing 

Lack of Incentives and 

Independence Issues Hinder 

Sharing between Program 

Offices and the Oversight 

Community
Page 15 GAO-13-680SP Data Analytics Forum

Standards if the IG is perceived as directing the agency to implement 

specific management practices and then later audits these practices.8

Participants stated that collaboration and cooperation between and 

among federal, state, and local stakeholders could help all levels of 

government prevent and detect fraud, waste, and abuse; but a number of 

challenges exist to sharing data and information between entities.

• State and local participants highlighted actual and perceived 

legislative barriers that limit federal sharing across levels of 

government. One participant with experience working for a local 

government recalled a recent instance when city auditors who were

performing an audit related to the city’s evaluation of client services 

for its human immunodeficiency virus (HIV) services program 

encountered legal barriers. Specifically, city auditors were evaluating 

whether HIV services jointly paid for by the city, state, and federal 

government were actually being received by clients in need. The 

auditors requested client-verification data from federal and state 

agencies to assist them in their efforts; however, the agencies 

objected to providing data, citing concerns about the Health Insurance 

Portability and Accountability Act (HIPAA)—which was enacted in part 

to safeguard protected health information from unauthorized use and 

disclosure. Additionally, participants said that some oversight and law 

enforcement entities at state and local levels perceive barriers that 

may not actually exist. For example, some participants with 

experience working for state or local governments said that state and 

local entities do not have legal authority to access databases 

containing certain federal data, such as identity verification through 

the Social Security Administration, federal Do Not Pay lists, and 

prisoner data and incarceration records. However, one federal 

participant countered that state and local entities may believe they do 

not have such legal authority, when in actuality there may be no 

federal laws preventing state and local agencies from accessing some 

of the databases mentioned. For example, this participant said there 

is nothing in the law that prohibits state and local entities from 

accessing federal Do Not Pay lists. Such issues—whether they are an 

accurate interpretation of existing law or potentially incorrect 

perceptions—may hinder efforts to stop improper payments at the 

 

8

Steps to address this issue and other challenges identified at the forum are covered in 

the final section of this report. 

State and Local Participants 

Experienced a Number of 

Challenges to Sharing Data and 

Knowledge across Levels of 

Government 
Page 16 GAO-13-680SP Data Analytics Forum

state and local levels, according to participants.

• Some data are cost-prohibitive to obtain. One participant commented 

that the cost of commercial data is an impediment to developing and 

using analytics capabilities in the government. This participant 

explained that private-sector companies and some governmental 

agencies impose fees for access to their data. One example 

participants cited is the Social Security Administration’s Death Master 

File (DMF).9 The DMF can be used to ensure government benefits are 

not being provided to deceased individuals; however, an agency 

wishing to use the DMF must pay fees that some participants said 

were too costly for their agencies.10 One forum participant said that 

his office received a quote of over $50,000 for a 1-year subscription to 

the DMF with monthly updates.

• Limited coordination between federal, state, and local oversight 

entities sometimes causes missed opportunities and redundancy in 

auditing activities. State and local participants reported that 

information is not readily shared even when state or local 

governments are working toward the same goals as the federal 

government on related or identical efforts. For example, a participant 

with experience working for a local government described a situation 

in which the participant’s city was performing an audit of a municipal 

wastewater-treatment facility. The participant said that nearly identical 

audits were being performed simultaneously by federal and state 

auditors. Although auditors from each level of government were aware 

of the other concurrent audits, there was little effort to determine 

whether they could work together in an effort to avoid duplicative work 

and conserve scarce resources. This official also noted that there are 

instances in which a federal agency will conduct and complete an 

audit or investigation of a city office, with the city only to be informally 

notified after the audit or investigation has been done, which creates 

the potential for redundant audits. Additionally, a forum participant 

with experience working for a state oversight entity said that there is 

no mechanism within the federal government to identify and leverage 

 

9

The DMF is maintained by the Social Security Administration and contains approximately 

98 million records of deaths that have been reported since 1936. The file is used by 

government; credit-reporting organizations; and financial, investigative, medical research, 

and other industries to verify deaths.

10The Social Security Administration has statutory authority to require reimbursement to 

cover the reasonable cost of sharing the data, and the amount varies by agency.
Page 17 GAO-13-680SP Data Analytics Forum

the results of state-government audits in related federal investigations. 

Such a mechanism to share information would allow the federal 

government to incorporate relevant knowledge garnered at the state 

level, according to the participant.

• State and local government offices are sometimes reluctant to share 

data with the federal government. A forum participant with experience 

in local government oversight said that local governments may be 

reluctant to share more than the required amount of data with state 

and federal agencies because data sharing seemed to be a “one-way 

street,” with local offices providing referrals to federal and state 

entities but getting nothing in return.

During the forum, participants stated that there is a need to share 

analytical processes and algorithms to better benefit oversight and law 

enforcement communities. The discussion in particular concentrated on 

the benefits of utilizing interoperable, open-source analytical tools and 

techniques, including open-source software. One participant encouraged 

the use of open-source tools because they can be utilized at a low cost, 

can be adjusted to meet the needs of individual agencies relatively easily, 

and have large support communities that agencies can tap for guidance. 

Two participants also noted that the use of open-source tools could 

lessen the challenge of developing licensing agreements for proprietary 

software tools. Some participants suggested that open-source tools and 

algorithms could be consolidated in a central location to allow for ease of 

access across these communities to better identify fraud, waste, and 

abuse.

Throughout the day, forum participants identified a variety of challenges 

that hinder their abilities to share and use data, as well as several areas 

where opportunities may exist to improve their abilities to use data to 

identify fraud, waste, and abuse. During the final session of the forum, 

participants shared their views on how federal oversight and law 

enforcement agencies might address these challenges and capitalize on 

opportunities. Participants discussed ways federal agencies could raise 

awareness of available data, improve access to information, and identify 

statutory changes that may be necessary to overcome challenges 

associated with sharing and matching data. Specifically, participants 

suggested the following next steps:

Opportunities Exist to 

Develop, Enhance, and 

Leverage Open-Source 

Data-Analytics Modules 

and Tools to Facilitate 

Knowledge Sharing

Forum Participants 

Identified Next Steps 

in Addressing 

Challenges 
Page 18 GAO-13-680SP Data Analytics Forum

• Compile a consolidated directory of data sources. One theme that 

emerged during the forum is that oversight and law enforcement 

entities are often not aware of the data at other federal agencies or 

publicly available data that could aid their missions. Participants 

discussed the benefits of a detailed directory of existing data sources 

used to identify fraud, waste, and abuse that could be referred to by 

oversight and law enforcement entities. CIGIE and the Recovery 

Board agreed to colead an effort to provide a means to increase 

awareness of useful datasets.

• Compile a dataset of known offenders. During the forum, participants 

discussed the need for greater sharing of information, particularly on 

known offenders. Oversight and law enforcement entities have casemanagement systems, but participants cited reluctance to share data 

on known offenders. Participants believed that centralizing information 

on such individuals—while bearing in mind any potential privacyprotection concerns—would yield benefits in the detection and 

prevention of fraud. CIGIE and the Recovery Board agreed to colead 

an effort to increase awareness of known offenders.

• Address statutory challenges related to data access and use. 

Participants discussed the importance of examining the existing 

statutory framework to determine whether statutory changes would be 

useful to oversight and law enforcement agencies in carrying out their 

missions. CIGIE reported that it has an existing committee to analyze 

legislation and, as deemed necessary by its participants, develop 

legislative proposals recommending changes to existing and 

proposed statutory provisions. CIGIE offered to make this existing 

committee available as a vehicle to expand a discussion of current 

law, and, as appropriate, to continue to identify changes to existing 

and proposed statutory provisions that create challenges and barriers 

affecting audit and law enforcement data access and use.

• Address questions about the Government Auditing Standards 

independence requirement. The IG community raised questions about 

the extent to which they can share results with agencies that would 

help agencies prevent fraud, waste, and abuse, and yet still maintain 

independence necessary for conducting effective oversight. 

Participants saw value in having GAO—which develops and issues 

the Government Auditing Standards—provide clarification regarding 

the independence standard. As a result, GAO agreed to provide 

clarification related to the independence standard of the Government 

Auditing Standards. 
Page 19 GAO-13-680SP Data Analytics Forum

• Develop an ongoing community of practice focused on data-sharing 

challenges. Throughout the forum, participants cited the need for 

greater coordination and incentives to share information among 

federal, state, and local government agencies as well as among 

federal government agencies. For example, one participant said that 

there is no mechanism within the federal government to identify and 

leverage the results of state-government audits in related federal 

investigations. Another participant said that agencies that invest a lot 

of resources into developing data may have cultural resistance to 

giving it away for free. To address these and other issues related to 

coordination and data sharing, GAO will be forming a community of 

practice that will address issues related to data and information 

sharing across federal, state, and local governments, as well as 

sharing data among federal agencies.

• Compile a library of available open-source data analytics, modules, 

and tools. Forum participants said that sharing algorithms and 

analytics tools would help to improve efficiencies and suggested that 

consolidating open-source software, algorithms, and data-analytics 

tools could help oversight entities in their audits, inspections, 

evaluations, and investigations. CIGIE and the Recovery Board will be 

working together to compile and maintain this library.
Appendix I: Forum Agenda

Page 20 GAO-13-680SP Data Analytics Forum

Appendix I: Forum Agenda
Appendix II: Forum Participants

Page 21 GAO-13-680SP Data Analytics Forum

Appendix II: Forum Participants
Appendix II: Forum Participants

Page 22 GAO-13-680SP Data Analytics Forum
Appendix II: Forum Participants

Page 23 GAO-13-680SP Data Analytics Forum
Appendix III: Related Products and Image 

Sources

Page 24 GAO-13-680SP Data Analytics Forum

Social Security Administration: Preliminary Observations on the Death 

Master File. GAO-13-574T. Washington, D.C.: May 8, 2013.

Human Services: Sustained and Coordinated Efforts Could Facilitate 

Data Sharing While Protecting Privacy. GAO-13-106. Washington, D.C.: 

February 8, 2013.

Medicare Fraud Prevention: CMS Has Implemented a Predictive 

Analytics System, but Needs to Define Measures to Determine Its 

Effectiveness. GAO-13-104. Washington, D.C.: October 15, 2012.

Privacy: Federal Law Should Be Updated to Address Changing 

Technology Landscape. GAO-12-961T. Washington, D.C.: July 31, 2012.

Federal Statistical System: Agencies Can Make Greater Use of Existing 

Data, but Continued Progress Is Needed on Access and Quality Issues. 

GAO-12-54. Washington, D.C.: February 24, 2012.

Improper Payments: Moving Forward with Governmentwide Reduction 

Strategies. GAO-12-405T. Washington, D.C.: February 7, 2012.

TANF and Child Welfare Programs: Increased Data Sharing Could 

Improve Access to Benefits and Services. GAO-12-2. Washington, D.C.: 

October 7, 2011.

Privacy: Alternatives Exist for Enhancing Protection of Personally 

Identifiable Information. GAO-08-536. Washington, D.C.: May 19, 2008.

Data Mining: Federal Efforts Cover a Wide Range of Uses. GAO-04-548. 

Washington, D.C.: May 4, 2004.

Phyllis K. Fong, Inspector General, U.S. Department of Agriculture. 

American Recovery and Reinvestment Act—Review of the Effectiveness 

of Department/Agency Data Quality Review Processes. Memorandum to 

the Recovery Accountability and Transparency Board. Washington, D.C.: 

June 25, 2010. http://www.usda.gov/oig/webdocs/50703-2-DA.pdf

Office of Inspector General, U.S. Department of Agriculture. American 

Recovery and Reinvestment Act—Review of the Effectiveness of 

Department/Agency Data Quality Review Processes. Audit Report 50703-

1-DA. Washington, D.C.: June 2010. 

http://www.usda.gov/oig/webdocs/50703-1-DA.pdf

Appendix III: Related Products and Image 

Sources 

GAO

Council of the Inspectors 

General on Integrity and

Efficiency 
Appendix III: Related Products and Image 

Sources

Page 25 GAO-13-680SP Data Analytics Forum

Office of Inspector General, U.S. Department of Education. Recovery Act 

Data Quality: Recipient Efforts to Report Reliable and Transparent 

Information. Memorandum to the Recovery Accountability and 

Transparency Board. Washington, D.C.: September 13, 2010. 

http://www2.ed.gov/about/offices/list/oig/auditreports/fy2010/s20k0002.pdf

Office of Inspector General, U.S. Department of Agriculture. Analysis of 

FNS’ Supplemental Nutrition Assistance Program Fraud Prevention and 

Detection Efforts. Audit Report 27002-0011-13. Washington, D.C.: 

September 2012. http://www.usda.gov/oig/webdocs/27002-0011-13.pdf

Office of Inspector General, U.S. Department of Education. American 

Recovery and Reinvestment Act: The Effectiveness of the Department’s 

Data Quality Review Processes. Final Audit Report, ED-OIG/A19K0010. 

August 2011. 

http://www.recovery.gov/Accountability/inspectors/Documents/a19k0010-

Effectiveness%20of%20Dept's%20Data%20Quality%20Review%20Proce

sses.pdf

Office of Inspector General, U.S. Department of Transportation. Recovery 

Act Data Quality: Errors in Recipients’ Reports Obscure Transparency. 

Washington, D.C.: February 23, 2010. 

http://www.recovery.gov/Accountability/inspectors/Documents/Data%20Q

uality%20Phase%20II%20Final%20Report.pdf

Office of the Inspector General, U.S. Department of Justice. Review of 

Department of Justice Data Quality Procedures for Recovery Act 

Recipient Reports. Washington, D.C.: October 2009. 

http://www.recovery.gov/Accountability/inspectors/Documents/DOJ%20D

ata%20Quality%20Review%20Report%2010-30-09%20Final.pdf

Solutions for Accountability and Transparency: Uniform Governmentwide 

Award ID Number. n.d. 

http://www.recovery.gov/About/Documents/WhitePaperonStandardizedG

overnmentwideAwardID.pdf

National Academy of Public Administration. A Recovery Dialogue on IT

Solutions: After-Action Report. A report prepared for The Hon. Earl E. 

Devaney, Chairman, Recovery Accountability and Transparency Board, 

and G. Edward DeSeve, Special Advisor to the President, Recovery 

Implementation. Washington, D.C.: May 2009. 

Recovery Accountability 

and Transparency Board
Appendix III: Related Products and Image 

Sources

Page 26 GAO-13-680SP Data Analytics Forum

http://www.recovery.gov/About/Documents/NAPA_Recovery_Dialogue_Fi

nal_Report_5-20-09_0.pdf

This section contains credit and copyright information for images and 

graphics in this report. 

Front cover: PhotoDisc (globe), GAO (GAO logo), Council of the 

Inspectors General on Integrity and Efficiency (CIGIE logo), Recovery 

Accountability and Transparency Board (Recovery Board seal) 

Highlights page: PhotoDisc (globe), GAO (GAO logo), Council of the 

Inspectors General on Integrity and Efficiency (CIGIE logo), Recovery 

Accountability and Transparency Board (Recovery Board seal)

Image Sources
Appendix IV: Contacts and Staff 

Acknowledgments

Page 27 GAO-13-680SP Data Analytics Forum

Steve M. Lord, (202) 512-4379 or lords@gao.gov

Mark Jones, (202) 292-2600 or CIGIE.Information@cigie.gov

Michael Wood, (202) 254-7900 or michael.wood@ratb.gov

In addition to the contact named above, Patricia Dalton, Chief Operating 

Officer; Nabajyoti Barkakati, Chief Technologist; Cathleen A. Berrick, 

Managing Director; Rick Hillman, Managing Director; Erika Axelson, 

Assistant Director; Jennifer Costello, Assistant Director; Joah Iannotta, 

Assistant Director; Jamie L. Berryhill; Marcus Corbin; Ranya Elias; Robert 

Graves; Linda Miller; and Maria McMullen made important contributions 

to organizing the forum and producing this report.

In addition to the contact named above, Gary Cantrell and Steve Shandy, 

U.S. Department of Health and Human Services Office of Inspector 

General; Charles Coe, U.S. Department of Education Office of Inspector 

General; and Rod DeSmet; U.S. Department of Agriculture Office of 

Inspector General, made important contributions to organizing the forum 

and producing this report.

In addition to the contact named above, Jenny Rone, Senior Advisor for 

Accountability, made important contributions to organizing the forum and 

producing this report.

Appendix IV: Contacts and Staff 

Acknowledgments 

Contacts 

GAO

Council of the Inspectors 

General on Integrity and 

Efficiency 

Recovery Accountability 

and Transparency Board

Staff 

Acknowledgments 

GAO

Council of the Inspectors 

General on Integrity and 

Efficiency 

Recovery Accountability 

and Transparency Board

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