PRINCIPLE 4 — Comparable and Interoperable

Authors: Stephen Gates | Reviewers: Ana Brandusescu, Danny Lämmerhirt
For open data to be effective and useful, it should be easy to compare and combine within and across sectors, geographies, and time. Principle four states that data should be structured and standardized to support interoperability, traceability, and effective reuse.

Case study

Datos Abiertos, Transparencia y Acceso a la Inform (DATA) Uruguay, an open data civil society organization, partnered with the Uruguayan Ministry of Health, a Charter adopter, to create A Tu Servicio, a website to compare local health care providers. This required health care providers to supply data in an open format, following a consistent standard to enable citizens to make an informed decision about retaining or changing their health care provider. The success of the project encouraged the Ministry of Health to make a commitment to open data, establishing new standards for openness and quality, and publishing new information about how data is collected, stored and made available for reuse.

Overview of Principle 4 – Comparable and Interoperable

Target audience (s)
Governments (Open Data Publishers)
What is currently measured
Commitments P4.a to P4.c are measured to some extent. The existence of standards-compliant, machine-readable metadata can be automatically assessed.
Elements of Principle 4 that are assessed by leading open data measurement tools are catalogued in Appendix I - Principle 4 Indicator Table and reviewed below.
Commitment P4.a, “Implement consistent, open standards related to data formats, interoperability, structure, and common identifiers when collecting and publishing data”, is measured by ODB and OURdata. Indicators assess the evidence of the adoption of international standards and practices, and whether data is published in compliance with those standards, and with unique identifiers.
Commitment P4.b, “Ensure that open datasets include consistent core metadata and are made available in human- and machine-readable formats”, is measured by ODB, OURdata, and ODIN. Indicators assess the adoption of metadata standards and a data quality control process that is assumed to share data quality information.
Commitment P4.c, “Ensure that data is fully described, that all documentation accompanying data is written in clear, plain language, and that data users have sufficient information to understand the source, strengths, weaknesses, and analytical limitations of the data”, is partly measured by ODB and OURdata. Indicators assess the existence of documentation describing the data.
Commitment P4.d, “Engage with domestic and international standards bodies and other standard setting initiatives to encourage increased interoperability between existing international standards, support the creation of common, global data standards where they do not already exist, and ensure that any new data standards we create are, to the greatest extent possible, interoperable with existing standards”, is not measured.
Commitment P4.e, “Map local standards and identifiers to emerging globally agreed standards and share the results publicly”, is not measured.