Modernizing Government Data Systems: Better Code Means Better Infrastructure

Government agencies depend on data systems every day to produce statistics, evaluate programs, allocate resources, and inform policy. But behind many of these mission-critical data products are legacy systems built over years, or decades, using different software, manual processes, and code written and maintained by different team members.

Our new white paper, “Modernizing Mission-Critical Government Data: From Legacy Code to Reproducible, Open-Source Infrastructure,” examines what it takes to modernize these systems and the lessons learned through Coleridge’s multi-year collaboration with the U.S. Department of Agriculture (USDA).

Three Building Blocks for Modernization

The paper identifies three core elements that can help agencies move from fragmented legacy processes toward reproducible analytical workflows.

Start with clear documentation: Before rebuilding a system, agencies need to understand how it actually works. That means mapping inputs, outputs, code, data sources, manual processes, and the people responsible for different parts of the workflow. The paper argues that creating this clear pipeline map is often the most important first step.

Automate manual processes: Repetitive tasks such as downloading spreadsheets, copying information between programs, or manually updating datasets create inefficiencies and opportunities for error. Where appropriate, APIs, automated validation, and other tools can replace these steps and create more consistent workflows.

Move toward unified, open-source code: Consolidating processes previously spread across proprietary tools into languages such as R or Python can make systems easier to maintain, document, test, and adapt.

Lessons from USDA

These principles were put into practice through Coleridge’s work with USDA’s Economic Research Service to modernize processing for the Agricultural Resource Management Survey and Agricultural Productivity data products.

One significant transformation was moving from large, sequential scripts containing thousands of lines of code to modular pipelines composed of smaller, reusable functions. The new approach also incorporated automated testing and documentation directly into the codebase, making it easier to identify errors, understand how variables are constructed, and maintain the system over time.

For Agricultural Productivity data, modernization also replaced manual collection and processing across multiple tools with automated data retrieval and a centralized open-source workflow.

Read the Report