XBRL US commented on the 6th Open Government National Action Plan. The request for public comment which was published by the Office of Government-wide Policy (OGP); General Services Administration (GSA) aimed to gather ideas, suggestions, and recommendations for commitments that could be included in the Open Government National Action Plan.
The XBRL US letter focused on fiscal openness, one of the ten Open Government Challenge Areas, that seeks to “advance public oversight and inclusion reforms across the budget and spending cycle“. The request for public comment asked respondents to focus their recommendations about their selected challenge area on “Problem Identification“, “Opportunities to Build on Existing Work“, “Innovative Approaches” or “Resources and Recommendations“.
Our letter addressed three of the four categories, starting with Problem Identification. Legislators and regulators at both a federal, state, and local level need more efficient access to budget data. Today, financial statement, budget, and debt-related data is reported in paper-based formats such as PDF files. The lack of machine-readable data requires policymakers, oversight agencies, and regulators to manually rekey and validate data before analysis can be conducted. Data standards automate reporting, collection, and analysis, improving consistency, timeliness, and quality of reporting.
Open data standards also represent an opportunity to build on existing work. Governments reporting financial statements and budget data in the same format will greatly enhance the usability of all data reported. Making Federal Audit Clearinghouse (FAC) data machine-readable would also satisfy the requirements of the Grants Reporting, Efficiency and Transparency (GREAT) Act which was passed in 2019 but has yet to be fully implemented. Since the General Services Administration (GSA) is now responsible for FAC development, it is well positioned to deliver on the promise of fiscal openness.
Collecting data in structured, machine-readable format also supports innovative approaches like artificial intelligence and machine-learning technologies because granular, highly understandable data will provide better outcomes in AI platforms.
Read the letter: XBRL US Comment RE Docket GSA 2024 0016
