Best Practice: Enable quality assessment of open data

Outline

Improve the trust in government by enabling assessment and/or providing evidence related to the quality of the published information/data.

Links to the Revised PSI Directive

Data Quality

Challenge

Whether a dataset is of sufficient quality to be used for a specific task will depend entirely on the task in question. There is no single objective measure of quality. However, work carried out in the European Commission's Open Data Support project suggests 7 aspects to consider:

• Accuracy: is the data correctly representing the real-world entity or event?
• Consistency: Is the data not containing contradictions?
• Availability: Can the data be accessed now and over time?
• Completeness: Does the data include all data items representing the entity or event?
• Conformance: Is the data following accepted standards?
• Credibility: Is the data based on trustworthy sources?
• Processability: Is the data machine-readable?
• Relevance: Does the data include an appropriate amount of data?
• Timeliness: Is the data representing the actual situation and is it published soon enough?

Users of data are likely to be making their assessment of whether the information and data available are sufficient for their needs based on these criteria. It is a challenge to understand what is required in terms of data quality and define a set of basic and measurable metrics to determine data quality in an objective way.

Solution

Implement a dataset publication pipeline that includes quality assessment and provenance information alongside the published data. The W3C Data Quality Vocabulary provides the means to make such information available in a machine readable and interoperable manner, covering:

• annotations that describe the data's quality;
• computed quality metrics;
• certificates that describe the dataset production pipeline;
• provenance information using the W3C PROV standards.

Best Practice Identification