Government transparency is a widely agreed upon goal, but progress on achieving it has been very limited. Transparency promises from political leaders such as President Barack Obama and House Speaker John Boehner have not produced a burst of information that informs stronger public oversight of government. One reason for this is the absence of specifically prescribed data practices that will foster transparency.
Four key data practices that support government transparency are: authoritative sourcing, availability, machine-discoverability, and machine-readability. The first, authoritative sourcing, means producing data as near to its origination as possible — and promptly — so that the public uniformly comes to rely on the best sources of data. The second, availability, is another set of practices that ensure consistency and confidence in data.
The third transparent data practice, machine-discoverability, occurs when information is arranged so that a computer can discover the data and follow linkages among it. Machinediscoverability is produced when data is presented consistent with a host of customs about how data is identified and referenced, the naming of documents and files, the protocols for communicating data, and the organization of data within files.
The fourth transparent data practice, machine-readability, is the heart of transparency, because it allows the many meanings of data to be discovered. Machine-readable data is logically structured so that computers can automatically generate the myriad stories that the data has to tell and put it to the hundreds of uses the public would make of it in government oversight.