Is the Global Open Data Index currently an active annual ranking?

The official project archive identifies it as inactive. Its published historical editions should be labeled with their dates.

Does the index measure the quality of every government dataset?

No. It assessed selected publication categories and criteria. Accuracy and task-specific suitability require additional investigation.

Can another organization create an open data index?

Yes, but it should publish its own scope, criteria and method and avoid implying equivalence with the Global Open Data Index merely because the name sounds similar.

AI Glossary / Data science and analytics / Intermediate

Open data index

Definition

An open data index is a structured assessment of how openly selected datasets are published. In this entry's source context, the term refers to the Global Open Data Index, an Open Knowledge initiative assessing government data publication from a civic-user perspective.

What it means

The Global Open Data Index examined selected categories of government data rather than every dataset a government might hold. Its criteria considered practical access, technical usability and permission for reuse. Publishing a page that people can view is different from providing data they can obtain and process under open terms.

The official project site now identifies the project as archived and inactive. Its 2016/2017 material is therefore historical evidence, not a current ranking of governments. Earlier editions also used different methods, which limits simple comparisons over time.

For AI and business users, the index is useful as an example of evaluating data availability systematically. It does not certify that a dataset is accurate, representative, appropriate for a model or currently maintained.

How it works

An assessment defines which data categories and characteristics are in scope, gathers evidence about their publication and evaluates that evidence against stated questions. The historical Global Open Data Index used contributions and review to assess publication from a user's perspective.

Its methodology distinguishes legal, technical and practical aspects of openness. Relevant questions include whether data can be obtained, whether its format supports processing, whether it is timely and whether its stated terms allow open reuse.

Results must be read with the method and underlying evidence. A partial score can show progress on some criteria while important barriers remain. The 2016/2017 methodology explicitly warns against treating percentages as a simple linear measure of openness. A score is an assessment under a stated framework and date, not an unconditional permission attached to every future version of the source.

Three practical examples

1. Dataset discovery: an analyst uses a historical assessment to identify potentially useful government-data sources, then checks the current source directly before relying on it.

2. Publication improvement: a public-data team examines whether users can download and process a dataset, instead of measuring success only by the existence of a portal page.

3. AI project review: a team separates data-access and reuse questions from model-readiness questions such as coverage, label quality, provenance and update frequency.

Think of it this way

Think of a structured inspection of a library's access arrangements. It checks whether materials can be found, obtained and used under stated conditions. It does not establish that every book is correct or that the inspection remains current forever. Similarly, an open data index assesses selected publication characteristics within a defined scope and period.

Worked example

Consider two fictional transport-data releases. Release A is a publicly viewable chart showing monthly totals, but provides no downloadable underlying records. Release B provides a downloadable, machine-readable dataset, a clear update date and stated open reuse terms.

A publication assessment can distinguish their practical usefulness for automated analysis. An AI pipeline could ingest Release B more directly, while Release A may require extraction and may lack the underlying detail needed for the task.

However, suppose Release B's records cover only half the transport network. Its accessible format does not make it representative of the whole network. The project must check coverage separately.

This is an original comparison, not an official Global Open Data Index score. No ranking is assigned because the historical methodology would require its complete dataset criteria and evidence, not just these simplified descriptions.

Illustrative case study

Illustrative fictional case: Ashbridge Analytics includes an old Global Open Data Index ranking in a proposal and describes it as the country's current open-data position. A reviewer notices the historical edition and the archive notice.

The team relabels the material as a historical benchmark, checks the current datasets needed for the project and records their publication dates, formats and terms. It also removes a claim that the ranking proves the data is suitable for training a forecasting model.

The fictional lesson is to preserve the distinction between historical benchmarking, present access and task suitability. The case does not claim that the index itself made the incorrect assertion or that one current dataset can represent an entire government's publication practice.

Common misconceptions and mistakes

Do not confuse an open data index with a database index that accelerates queries. Do not assume publicly visible data is necessarily openly reusable or available in a useful bulk format.

Do not present archived results as live rankings. Avoid comparing editions without checking changes in categories, questions and scoring. Do not treat a high aggregate score as proof that a particular needed dataset meets every requirement. Finally, openness is not a substitute for accuracy, privacy-aware handling or an appropriate understanding of the data's meaning.

Business use

Use the index as a historical reference and methodological example. For a current business or AI decision, inspect the exact source release and document the evidence relevant to the intended use.

A practical dataset record should distinguish discoverability, download access, format, stated reuse terms, freshness and content coverage. Assign responsibility for checking changes when the project depends on an external source. This approach turns a broad openness label into concrete questions while avoiding a misleading promise that an index score settles every operational or analytical concern.

Check your understanding

A dataset received a favorable historical openness assessment. Can an AI team skip checking its present download, update date and terms?

Answer: No. The assessment refers to a particular edition and evidence period. The current source and the project's actual requirements still need to be checked.

Related terms

Metadata
Benchmarks
Data Quality
Open data
open government data
machine-readable data
data portal
data provenance
benchmark
dataset licence

Learn next

Read the historical methodology and identify what it measures and excludes. Then inspect one current dataset and record access, format, update information and stated reuse terms separately from accuracy and coverage. Practice labeling benchmark results with their edition and scope.

Sources

Open Knowledge Foundation, Global Open Data Index project history: https://okfn.org/en/who-we-are/our-history/global-open-data-index/

Global Open Data Index, 2016/2017 methodology and interpretation of scores: https://index.okfn.org/methodology/index.html

Global Open Data Index, project background: https://index.okfn.org/about/index.html

Global Open Data Index, archive status and historical findings: https://index.okfn.org/insights/index.html

Official indexed text was reviewed; direct archive-page requests failed. The transport and proposal cases are fictional.

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