DATA Act Forum - ACT-IAC | Advancing Government DATA Act Forum DATAtho… · The goal of the...

Advancing Government through Collaboration, Education and Action

DATA Act ForumJuly 27-28, 2015

DATAthon


DATAthon July 27-28, 2015

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The goal of the DATAthon is to build knowledge from open data and inspire DATA Act Forum

participants to realize greater value from data, particularly financial open data.

As the DATA Act makes federal financial data “accessible, discoverable, and useable” for public

consumption, opportunities are emerging to leverage this data within the government and in the

private sector. Volunteers will demonstrate the “art of the possible” with government data. A

team of over 20 data professionals from across government, industry and academia worked

side-by-side to conduct inquiries into enabling taxpayers and officials to more effectively track

spending; prevent fraud, waste, and abuse; and reduce regulatory burden.

Teams were supported with data resources, including access to a database with much of the

currently available public data described in the DATA Act, as well as access to commercial data

services generously donated by IAC members.


The Data

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Summary of Data and Infrastructure

DATAthon participants will have access to rich datasets that are related to the DATA Act, as well as robust

technology platforms including a Teradata Aster Platform, D&B’s Direct 2.0 API, and Socrata’s Community

Data Platform. Participants are not limited to using this infrastructure. Below are the base dataset details that

will be provided to DATAthon participants to show the art of the possible:

1. Budget - 4400 rows (Agency, Bureau, Account Name, TAS Agency, Account Code, 1976 thru 2020)

2. CFDA - 4000 rows

3. Object Class by Agency - 1000 rows

4. Outlays - 5000 rows (Agency, Bureau, Account Name, TAS Agency, Account Code, 1976 thru 2020)

5. TAS-BETC - 300k rows (TAFS)

6. SF133 (2010-2014) - 250k rows * 5 = 1 mil rows (Agency, Bureau, Account, TAS Agency, Account Code,

Line Number, Description, Quarter breakdown of amounts)

7. Awards - 20 mil records approx. (250 columns) - contracts, grants, direct payments, insurance, loans,

other assistance

Participants may augment this data with data from other open data sources or APIs.

The IdeaScale platform was used to generate ideas and challenges and ultimately crowdsource the topics for

the event.

Advancing Government through Collaboration, Education and Action 4

Thank you for the support of the DATAthon!


Thank you DATAthon volunteers!


Goal: Gain better insight on grants awarded towards programs related to improving conditions of

inmates and helping in the reintegration of ex offenders into society.

The upper panels showcases an overall view across the U.S on how much grant funding goes towards

each type of program per state. The circles represent the sum of overall grants. This data compiles grants

from all the years available in the data as a whole. Further development of the tool would refine this to

specify amount of each year.

The lower left panel displays information

about the top recipients of grants. The

number within each bar represents the

recipient's viability score.

The viability score is very valuable

information that is retrieved based on the

organization's Duns number/ The score tells

us the probability that a company will no

longer be in business, within the next 12

months, compared to all US businesses

within the D&B database. This information

tells us the health of the institution receiving

grant funding.

The lower right panel shows a line graph

representing the amount of grants per state

from 2010 to 2013 vs the population of the

prisons.

In the cases displayed we can see just how

much the grant funding has taken a dip

through the years, while the prison population

has remained steady or decreased slightly in

some cases.

Description below is for dashboard images on next page.


Goal: Examine how Contract and Grant spending trends over the course of a fiscal year and explore

how the makeup of this spending changes with respect to bid competitiveness, the number of bidders,

the relative size of the awards, and more.

A story of dashboards was created to convey our exploration and conclusions. In the end, we

concluded that Federal contracts and grants are awarded unevenly throughout the year - a very

large percentage are awarded in Quarter 4 - and that this unevenness coincides with a decrease

in bid competitiveness, spurs a 'substitution' effect between bids competed freely and openly with

other types of competition, and results in a decline of Grant 'continuation actions' while new

actions rise instead.

For further analysis, the team would look at this data geographically to understand how the

relative distributions of contracts and grants change over a fiscal year based on their location. If

our early analysis is any indication, we expect some interesting insights to emerge.


Goal: Answer three analytical questions: How strongly are CFDA programs associated; i.e. are there

pairs of programs where many recipients receive funding from both? Can we trace and visualize the

lifecycle flow of funds in a single graphic, i.e. path analysis? What are the most common sequences of

events in the history of a contract?

• cFilter finds strength of association among every pair of values:

– Large (N2) combinatoric problem: 86.4K pairs of values for ‘cfda_program_title’

– Solved quickly and visualized interactively in 2D

• Applied in the DATAthon:

– Data: All Direct Payments

– Fields: ‘cdfa_program_title’ field, as linked by the ‘recipient_name’ field.

• Result: When a pair of programs are linked, that means a significant number of recipients are getting

payments from both those programs. The links:

• red=strongest

• orange=medium

• yellow=least strong

How Strongly are CFDA Programs Associated Amongst Recipients?


Strongest Associations / Collaborative Filter

Red = Strongest

Orange = Medium

Yellow = Least Strong


Thickness of the line indicates

dollar amount. As the user hovers

over a line, it displays the amount

throughout the flow.

• Create a Sankey (flow) diagram.

• Applied this to “All Grants” data.

• User can select Agency and number of CFDA programs (top x or all) per agency.

• Result: produces visual overview of the total fund flow by agency, program, fiscal year and dollar

amount.

Tracing and Visualizing the Flow of Funding


Thickness of the line indicates number

of contract events.

What are the Most Common Sequences of

Events in the History of a Contract?Agency selected, Department of Homeland Security with contract modifications as events.


Goal: Analyze potential risk associated with Year-End spending on government contracts.

Observation: More dollars are obligated to government contracts at the end of the fiscal

year than any other time. Using government contract data from USAspending.gov, our

team observed that most contracts (in dollars and count) tended to be awarded at the

end of the fiscal year. We believe this effect is due to the “use it or lose it” mentality that

if allocated agency funds are not used by year end, there is a fear they will be returned

the Department of Treasury and potentially cut from future budgets.

Note: the chart below uses a sample of the data, representative of the population.

Details will be discussed in a later slide.


Hypothesis: If there is a “use it or lose it” attitude to end of year contracts, could

the need for quick turnarounds make them risky?

Our team had a hypothesis that if contracts had to be awarded quickly at the end of the

fiscal year, at risk of lost funds, has there been any detrimental effect in judgment of

companies for which those contracts are awarded? To test this hypothesis our team did

the following:

Approach:

• Determined to use Dun and Bradstreet’s Financial Stress Score (FSS), a measure of

a company’s potential for failure (details available here), to help define potential risky

judgement in contract awards. Scores range from 1 – 5, lower scores indicate higher

probability of failure.

• Utilized Dun and Bradstreet API to pull the Financial Stress Score (FSS) for 1,000

companies based on their DUNS number. This sample accounted for roughly 50,000

contracts from October 2009 to September 2014.

• Performed a regression model using the month of the signed date as a predictor of

FSS.

• Developed a Tableau interface on data stored in Teradata Aster to view results of the

analysis, as well as compare FSS across other factors such as agency, geolocation,

type of contract, etc.

https://www.dnb.com/ecomp/help/scores.htm#q8


Findings: There is a significant drop in financial stability of companies awarded

contracts at the end of the fiscal year.

Our team determined that, based on the regression model of our sample, that there was

a significant drop in the Financial Stress Score of companies awarded contracts at the

end of the fiscal year. A contract awarded in August was 6% more likely to go to a

company with a score of 2 or lower, and 4% more likely in September. This may indicate

a greater risk in judgement of contract awardees due to the incentive and rush to

obligate funds before year end.


Next steps…

While the results are not conclusive that there is greater risk of year end contracts, our

team believes that we are on the right track to helping agencies understand potential

impacts to how contracts are managed based on year end spending motivations. Below

are a few next steps we feel would help with this goal:

• Expand our sample to include all contracts over the last few years.

• Explore FSS and year end spending relationships against other factors such as

awarding agency, type of business (small vs large), type of contract, etc.

• Explore alternative metrics outside of FSS for determining contract performance.

Another example could be to utilize CIO evaluation data for IT projects available on

data.gov.

• Appeal to agencies to make contract performance data more available to public,

ideally in a standardized way that lends itself to analyses such as this. Similar to CIO

evaluation mentioned above.

• Continue to develop a dashboards that allow agencies and interested public parties

to see where government contract spending is being allocated against performance

of those contracts.


More to come!

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Connect with the ACT-IAC DATA Act – Transparency in Federal Financials Community.

https://actiac.org/project/data-act-transparency-federal-financials-project

https://actiac.org/project/data-act-transparency-federal-financials-project

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