Data standards

Data standards are agreed-upon formats, field names, codes, and definitions that let two organizations, or two systems within the same organization, produce data that means the same thing and can be combined or compared without manual translation. In government, common examples include standardized geographic codes, common budget line-item categories, and shared schemas for transit or crime-incident data.

A standard differs from a single agency's internal data dictionary, which documents that agency's own fields; a standard is agreed across multiple parties in advance, so that, for example, every city publishing transit data to the same standard produces files that a single trip-planning app can read without custom handling for each city. Standards are usually maintained by a governing body, a national statistics office, an industry consortium, or a standards organization, that also manages how the standard changes over time.

Data standards matter most where data needs to move between organizations, powering open data portal publishing, civic tech applications, and cross-agency master data management efforts. The common pitfall is adoption drift: agencies nominally follow a standard but interpret an ambiguous field differently, or lag behind a version update, which silently reintroduces the inconsistency the standard was meant to solve and undermines the broader data governance effort around it.