Measuring open data maturity

In this section:

Measuring data maturity can be a way to drive a culture of continuous improvement. 

This section presents two options for assessing and improving data maturity.

Option 1 is an agency-level assessment tool that enables organisations to benchmark how well they govern, manage, publish and consume open data.

Option 2 has a technical and accessibility focus and provides a simple set of criteria for assessing the accessibility and usability of individual datasets.

Both tools add value to agencies who are looking to measure and improve their corporate data practices, cultures and use. 

Benefits of measuring data maturity

By using this tool to measure their data maturity agencies can:

  • baseline and benchmark their data practice
  • identify specific areas for improvement
  • develop practical strategies for development
  • track progress towards the vision outlined in the NSW Open Data Policy.

Maturity model option 1:  Organisational open data maturity

This model, based on a model developed by the Open Data Institute, focuses on an organisation’s capacity and support for data maturity.  The model assesses 15 actions across 5 themes.  The themes are: data management processes, knowledge and skills, customer support and engagement, investment and financial performance, strategic engagement.

Organisational Open Data Maturity Assessment

The following lists the key action areas under each theme. Agencies should download the Open Data Institute’s more detailed assessment grid for full definitions of the various maturity levels for each theme and action area, in order to complete maturity level assessments for each area.

Data management processes


Data is released and make publicly available according to an established organisational process.


Data is released in machine-readable, open formats.


Data governance processes, addressing issues such as data quality, integrity, monitoring and protection across the data lifecycle, are applied across all organisational datasets.


Standard processes consistently support the application of safeguards to prevent disclosure of sensitive data.



Knowledge and skills


Data is released and make publicly available according to an established organisational process.


Data is released in machine-readable, open formats.



Customer support and engagement


The organisation identifies and engages with potential users of its data.


Datasets are published with a standard set of supporting documentation and metadata.


The organisation provides support to and seeks feedback from public users of its data.


The organisation engages with the broader open data community to share experience and promote open data activities.



Investment and financial performance


The organisation monitors the financial costs and benefits of open data publication and re-use.


Investment in open data publication is driven by cost/benefit analysis.


Contracts include standard clauses to ensure there is clarity around rights and licensing for data re-use.



Strategic oversight


The organisation has an open data strategy aligned with corporate objectives that describes ongoing commitments to open data.


Data is managed as an asset and all datasets published from or use by the organisation are discoverable through the Data NSW open data catalogue.


Open data activities are listed in the agency's Agency Information Guide.



The following maturity assessment implementation recommendations can help to scope an agency’s maturity assessment into a program of work to further improve open data maturity:

  1. Identify an organisational lead for the maturity assessment — a thorough assessment will likely require input from across the organisation but there should be a clear lead who coordinates the assessment.
  2. Identify the scope — the maturity model can be used to assess individual departments or a whole organisation. We recommend beginning with an assessment of the whole organisation.
  3. Identify key participants — which people in the organisation may need to be involved to help answer specific questions or support the evaluation?
  4. Assess and score each activity — using the assessment grid, review each of the activities and identify the level of maturity achieved by the organisation. To qualify at a specific maturity level, the organisation should exhibit all of the described behaviours.
  5. Set appropriate targets — having conducted a baseline assessment, identify appropriate targets for improvement. This will involve either maintaining or improving the score for specific activities.
  6. Develop an action plan —based on the results and the targets, identify a plan for implementing improvements.
  7. Circulate results — share the results, targets and action plan within the organisation, including to those involved in supporting the assessment. Senior management support and review will be essential in helping to implement improvements. An organisation may also wish to share its results more widely.
  8. Set date for next assessment — the action plan should set a date for a further assessment. This will allow the organisation to monitor its progress.

Maturity model option 2: Five Star Plus 

This self-assessment model uses the criteria to assess the accessibility, processability and useability of individual datasets. A star is awarded at each level of maturity, with a maximum of five stars+ (plus) being awarded to a dataset when all six criteria are achieved. Each star level of maturity includes the benefits and criteria of the lower levels. The relevant star rating is then applied to the dataset when it is published as open data.

Level Criteria Benefits


Data is available on the web, in whatever format, to be freely used, modified, and shared by anyone for any purpose. The default licence for NSW Government is the Creative Commons Attribution 4.0 International (CC BY 4.0).

The data can be accessed, printed and stored locally. The data can be entered into any other system, can be manipulated and shared with anyone.


Data is also available as machine-readable structured/tabular data. Machine Readable Data is data in a format that can be automatically read and processed by a computer.

Structured data refers to data where the structural relation between elements is explicit in the way the data is stored on a computer disk.

The data can be directly processed used proprietary software to aggregate it, perform calculations, visualise it, etc.

The data can be exported into another (structured) format.


Data is also available as non- proprietary format or open format. An open format is a file format (see list) for storing digital data which can be used and implemented by anyone.

The data can be manipulated without proprietary software.


The data also uses open standards (5) from W3C (RDF and SPARQL) to identify things. Open Standards refer to a formalised collection of non-proprietary web standards and other technical specifications developed by the W3C.

The data can be linked from any other place (on the Web or locally) and can be bookmarked. Parts of the data can be reused. The data can be more easily combined with other the data.


Data is also linked to other people’s data to provide context. Linked Data is a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF.

Related data can be discovered, and the organisational categories between this data can be conveyed.


Data also includes supporting information about when & why data is published. Supporting information may include data quality statements and contextual information about the data, as well as use statements and any other information that helps users to understand and use the data.

Provides user with insight about context and how a particular dataset could be used and whether it can be compared with other, similar datasets.


Last updated: 19 June 2019