Assessing Pandemic Relief Impact With Help From Dun & Bradstreet
The Paycheck Protection Program (PPP), launched in early 2020 by the U.S. Small Business Administration (SBA), was destined to make history due to its scope and scale. Even before it rolled out officially, opinions about PPP’s potential impact were nearly inescapable in the daily news cycle.
As the SBA introduced this three-round, roughly $961 billion emergency loan effort, analysts from both the private and public sectors scrutinized a broad range of indicators and data to understand the convolved impacts of the pandemic and emergency funding. Public policy think tanks were interested as well, and many offered observations, reports, and insights on the ongoing economic recovery and small business trends.
As one of these research bodies worked to design a study focused on assessing PPP loan impacts, its team identified internal gaps in existing data and technologies. To help their organization meet the challenges of this crucial work, the team reached out to Dun & Bradstreet’s data scientists and specialists for assistance.
More Power for Processing and Modeling
Dun & Bradstreet’s experts worked closely with the researchers to understand their existing data, modeling environment, and reporting needs. Two challenges emerged from the discussions.
First, the think tank’s existing tech environment lacked the tools and capacity to process the volume of data and sophisticated code needed for the scope of the proposed study. The team critically needed the ability to interrogate a huge amount of data with many attributes, which would require more compute power than their existing network and servers offered. Secondly, the team lacked the foundational business data required to analyze the impact of PPP loans on small and medium businesses.
D&B Analytics Studio provided a solution to both of these challenges. The scalable architecture provides Analytics Studio users with access to millions of business records with deep analytical insights from the Dun & Bradstreet Data Cloud. Data Cloud sources are continually monitored for changes, and the Data Cloud is updated accordingly to ensure information is fresh. Within Analytics Studio, users can also upload and match their own data in a secured partition. With the platform’s robust modeling tools, developers and analysts have access to a variety of programming languages (Python, R, or SQL) in a SOC 2 compliant, ISO certified space.
Releasing Round One PPP Findings
Once Analytics Studio was in place, the research team collaborated with Dun & Bradstreet’s data scientists. By sharing their modeling tips and best practices, the Dun & Bradstreet data scientists helped the research analysts establish the analytical dataset and experimental design to fuel their data-driven work. Because Analytics Studio supplied a ready-to-use analytic environment, the researchers were able to begin testing new modeling assumptions, assess the results, and quickly iterate model adjustments to refine measurements of the PPP’s effects on the small business community.
After completing the detailed study of PPP’s first round, the think tank published its findings in the second half of 2020. The resulting paper, made available to both the government and the broader public sector, represented an important new evaluation of the PPP program. This innovative assessment offered a perspective on how effectively the program met its desired goals of supporting small businesses and retaining employees on payroll. It also included recommendations on how to improve upon the program in any future iterations. This study is now an important asset for policy planners, experts, and consultants.