#8 USAspending.gov Analytics-Which Contractors Performed the Best in Capturing Information Technology Spending Increases from GFY16-GFY19?

Gaining an Analytics Edge Using Federal Spending Open Data Series


Premise of this Blog Series: Publicly available “open data” sources and open-source technology analytics can provide information to reduce uncertainty when making critical business investment decisions.


In earlier Medium.com blog posts in this series, I focused on the mechanics of downloading and preparing USAspending.gov Federal contractor obligations (spending) data using Python and its ecosystem of open-source packages such as pandas and Dask. In this post, I demonstrate how a business analyst and strategist can use this information to analyze the data in ways that provide context for business investment decisions.


The Federal government codes every contractor obligation with a designator that indicates the predominant type of work in that contract obligation. Per the instructions in the “Federal Procurement Data System Product and Service Codes Manual”:

Big Picture Context

I usually start my analysis by calculating analytics for the broader outline of the study subject and then dig deeper from multiple angles and levels. Doing this also helps as a way to check whether the results make sense.

Note: GFY2020 is YTD through September 2020 with 3-month lag for DoD obligations

Exploring PSC_Cat “D” — IT and Telecommunications Spending

The first step is to restrict my analysis to a restricted view of the data. I select obligations where the df_PSC_Cat_D = df.query(“PSC_Cat == ‘D’”) and restrict to GFY16-GFY19. That takes ~2 seconds to extract that view from the ~46 million records.

  • DOD: 10.3%
  • Civilian Departments: 7.8%

Analysis of Contractors that Benefited from the Increased Spending

With the context at the Department and Agency level, we can explore which contractors were the most successful in capturing PSC_Cat D market share faster than market growth rates from GFY16-GFY19. The PSC_Cat D market three (3) year CAGR was ~8.9% (~20% faster per year than the overall Federal market grew, which is a big difference with compounding growth). For the ten (10) largest recipients of PSC_Cat D contractor obligations in GFY19, this is how they performed:

Exporting Data for Data Sharing with Colleagues

Once you get the data into a format and record size you need, you can easily export the data into CSV or Excel format files using the convenient pandas to_csv or to_excel functions. Excel and Google Worksheets have limits on the rows and columns, so you need to winnow the data down to a size that can be imported into those packages first. I frequently use Python pandas to organize and pre-process data and import that smaller dataset into Excel or Google Worksheets.


I hope this example inspires you to use some of these techniques to explore relevant topics to your business and the strategic and tactical business investments you must make. The ~280 columns of the original records include information on contract vehicles used, task order expiration dates, the number of bidders on a contract, set-aside status, “contracting agency” in addition to “funding agency,” and many more fields. Many important market insights can be extracted from this open-data source. You can assess how your business performs relative to other firms and guide your business investment decisions, such as selecting strategic accounts to invest in and significant business development pursuits.

Coming Attractions

I will explore more analytical and market topics in future blog posts — including more market-share trend analysis and NLP techniques using USAspending.gov data and mashups with other open data sources.

Strategy, emerging technology, innovation, and management advisor https://www.primehookllc.com/about-us.html, https://www.american.edu/kogod/faculty/ulstrup.cfm

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