This paper proposes frameworks for understanding the data standardization effort that is the focus of the Financial Data Transparency Act (FDTA). It recognizes that the function and computer implementation of data standards have evolved (i.e., the implementation in computer code and data encoding software of a data standard), and that to address this evolution the effort to define a modern data standard requires that both the data and the authoritative definition of the data standards be expressed as machine-readable semantic data.
In covering these topics, this paper aims to:
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Expand (or develop) the law’s text (Title LVIII of P.L. 117-263), specifically the meaning of data and the data standards as machine-readable.
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Introduce concepts and frameworks for understanding the policy and computer implementation challenges, as well as proposing options for how to address these challenges.
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Highlight existing technical approaches, especially widely-used, non-proprietary global standards for identifying, describing, and expressing semantic data. Semantic data refers to information that is structured and encoded with meaning, enhancing human and machine understanding and providing context for automated processing and analysis.
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Describe how these existing technical approaches support the conceptual frameworks described in this paper and drive disclosure modernization as they are detailed in the law. Disclosure modernization is the movement of compliance reporting from documents to machine-readable data.
