IRS
Industry: Public Sector
Department: Business Intelligence
Region: North America
language process developed to reduce thousands of labor hours spent finding and fixing contract description errors
transparency in the description and accuracy of procurement contracts
of concept in days
The U.S. federal government spends more than $3.5 trillion and issues over 5.7 million procurement contracts annually. In order to improve transparency on where and how taxpayer money is spent, the Digital Accountability and Transparency Act of 2014, or DATA Act, requires the U.S. Federal Government to transform its spending information into open data. The DATA Act directs the Office of Management and Budget (OMB) and the Department of Treasury to establish government wide data standards, specifically the Data Act Information Model Schema (DAIMS). This must include common data standards for reporting and payment information.
The Internal Revenue Service (IRS) is a sub-agency of the Treasury, whose primary functions are tax collection and tax code administration. In support of its mission, the IRS contracts with the private sector to buy services and products. In 2019, the IRS launched a program to identify innovative approaches to complying with the DATA Act. The program, known as Pilot IRS – DATA Act Improvements, has the following goals:
The IRS faces increasing pressure to achieve more with less. Over the past five years, the U.S. government has seen a 23% compounded annual growth rate in contract volume. As the amount of work increases, the IRS has also seen a 40% decline in its workforce. In addition to maintaining its current workload, the IRS is faced with correcting contract errors to ensure compliance with the DATA Act and other policies. Finally, many of these procurement processes are performed manually across various IRS systems. Although this story is about the IRS, the Federal Government faces similar challenges across its more than four hundred agencies.
Like most large enterprises facing the challenges of legacy systems and processes, the IRS wanted to explore new and innovative ways to improve its data and operations. From extensive work in both public and private organizations dealing with similar procurement compliance issues, we know that the average organization loses approximately 7 hours per data worker in productivity due to manual legacy data processes.
Federal procurement analysts are tasked with drafting contracts, which can be a grueling process. They must gather inputs from multiple systems and stakeholders while maintaining compliance with various federal acquisition regulations. As they dig through emails and folders for information to complete multiple pages and hundreds of data fields, they often find themselves trying to interpret vague, incorrect, and incomplete data. This can include whether an address is up to date, if an award description is meaningful or not, or if contract dates are accurate.
In some cases, analysts can perform reference checks with third-party websites, while in other cases, they send emails, make phone calls, and schedule meetings to confirm the data they need. In all cases, they try to make the best decision possible with the limited information they have.
As analysts validate the new information they have gathered, they can only hope that all the information they’ve received from upstream processes have been adequately validated. As their long day comes to an end, they notice the large backlog of new contracts that just arrived in their queue, ready for tomorrow. They can think of dozens of ways to improve this process, but they believe it will require software development and data engineering resources. It just feels like it will be too complicated and take too long before they can see any meaningful improvement.
ResonantLogic, an Alteryx partner, developed a proof of concept (POC) to address this problem under the Pilot IRS program. ResonantLogic used Alteryx Designer and Alteryx Server to develop a working proof of concept for the IRS in less than 20 days. The solution used the following methods to:
Connect
Verify and Validate
Correct
Notify
Automation of mundane tasks and processes
Ability to integrate and connect with other systems
Improvement of data quality and greater transparency