Data Standards: Rosetta Stones for the Digital Age

Technical Brief

October 2024

Author: Dean Ritz, Senior Fellow, Data Foundation

Disclaimer: This paper is a product of the Data Foundation. The findings and conclusions expressed by the authors do not necessarily reflect the views or opinions of the Data Foundation, its funders and sponsors, or its board of directors.

Disclosure: The author made use of generative AI tools to assist his research and drafting.

Data Standards: Rosetta Stones for the Digital Age

Data standards play a crucial role in ensuring efficient and accurate communication and information exchange, as they serve as the “Rosetta Stone” for data. Data standards consist of two parts: 1) a conceptual model that captures the meaning and relationships of each piece of data in a way that humans can understand, and 2) a machine-readable implementation that expresses the conceptual model in a format that computers can process and share. Data standardization efforts primarily, though not trivially, involve documenting in a machine-readable format the knowledge already understood by people, as reflected in existing information collection requirements and practices.

This technical paper on data standards provides some simple examples of machine-readable data standards lined up with the human-readable versions, showing that data standards are just a different form of documentation for the knowledge we already have.

A simplified Data Rosetta Stone [see full paper for illustration] expresses two named concepts in three scripts: a human readable one, and two machine-readable ones. They are three faithful, consistent expressions of the same information.

XML and JSON are machine-readable data languages that organize data intro structures. They have different approaches. XML organizes data into a hierarchical structure using nested tags, which can be predefined in a template for consistent computer readability. JSON  is a simpler approach that organizes its content into a key:value pairs. Both are widely used, and both provide a well-defined mechanism for organizing data into discrete, identifiable parts.

Data standards transform your documentation, one byte at a time, making people-readable information machine-readable.

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