The GAO Technology Assessment Design Handbook Meets Data Visualization

The Government Accountability Office (GAO) considers its technology assessment (TA) to be an essential component of the assistance it provides to Congress. The Technology Assessment Design Handbook, released in December 2019, describes the process of developing a technology assessment, selection approaches, and implementation challenges.

The TA Design Handbook also addresses ways technology can be used to aid data-driven decision making. On pages 42-43 of the handbook, there are descriptions of analytical approaches to analyzing data. We have added relevant data visualization illustrations to each of these descriptions to enhance the content for data analysts:

Interpretive Structure

Source: U.S. Geological Survey

Interpretive structural modeling shows a graphical relationship among all elements to aid in structuring a complex issue area and may help to delineate scope.


Source: US Geological Survey

Trend extrapolation is a family of techniques to project time-series data using specific rules and may be helpful in forecasting technology.


Source: MatthewSpaniol [CC BY-SA 4.0]

A scenario is a composite description of possible future states incorporating several characteristics and may be helpful in policy analysis.



Scanning methods, such as checklists, list factors to consider in a particular area of inquiry and may be helpful in identifying potential impacts.

Relevance Trees

Source: Herald of Free Enterprise [Public domain]

Tracing methods, such as relevance trees identify sequential chains of cause and effect or other relationships that help identify potential impacts.

Cross-Effect Matrices

Source: Sharonlnchrs [CC BY-SA 3.0]

Cross-effect matrices are two-dimensional matrix representations that show the interaction between two sets of elements and may be helpful when analyzing the consequences of policy options.

Simulation Models

Source: Argonne National Laboratory

Simulation models provide a simplified representation of a real system in order to explain the dynamic relationships of the system and identify impacts and forecasting technology.

Benefit-Cost Analysis

Source: Department of Energy

A benefit-cost analysis is a systematic, quantitative method of assessing the desirability of projects or policies when it is essential to take a long view of future effects and a broad view of possible side effects.

Decision Analysis

Source: Department of Homeland Security

Decision analysis aids in the comparison of alternatives by weighing the probabilities of occurrences and the magnitudes of their impacts, which helps determine impacts and assess policy options.


Source: Modaniel [CC BY-SA 4.0]

Scaling identifies potential impacts of an activity on a stakeholder group and qualitatively or quantitatively assesses and analyzes the possible effects.

Data Visualization in the Federal Government

Although the Technology Assessment Design Handbook named the analytical descriptions we shared, many other government organizations utilize data visualization to tell their stories. For example:

  • The Intelligent Transportation Systems, a program of the Department of Transportation, has visualizations on integration, communication, weather, and traffic interventions on its website.
  • The Federal Reserve System website has interactive data visualizations on household net worth, household debt, state and local pensions, and other financial statistics.
  • The Securities and Exchange Commission website displays data visualizations of market activity data such as corporate stocks and exchange-traded products as well as quote life data.
  • The Census Bureau, at this moment in time, has 43 eye-catching data visualizations of health, recreation, occupation, language, population density, and many more variables.

Where have you seen magnificent examples of data visualization? Do you have any go-to websites for inspiration? We’d love to hear what you’ve got to say about how the Federal Government can use data visualization to tell their stories.

Thanks to Charley Tichenor for contributing to this blog.

Written by:
Natalie Komitsky
Media Type:

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