Challenges of open data quality: more than just license, format, and customer support.

Corsar, David; Edwards, Peter

Abstract

Public sector organisations worldwide are implementing Open Data initiatives, which, it is hoped, will stimulate economic growth, increase transparency and accountability, and improve engagement between data consumers (typically citizens) and data holders/publishers (Open Knowledge 2012). Open Data is defined as data that "anyone can freely access, use, modify, and share for any purpose." As developers have started using open data within their applications, they are reporting quality issues with such datasets that have subsequently been addressed by the data publishers. These include OpenStreetMap developers correcting the location of 18,000 UK bus stops and users identifying errors and omissions in data relating to UK registered charities. There is a growing recognition among the open data community that, to maximize the impact of such initiatives in terms of economic growth and increased accountability, focus must shift from publication of data to issues such as coverage, openness, and quality. Definitions of quality in the open data context vary considerably; for example, the European Data Portal considers data to be of high quality if "humans can understand it and machines can manipulate it" and point to the 5 Star Open Data rating system as a data marque. Others, such as the G8 Open Data Charter and the Open Data Institute Certification Badges focus on the provision of metadata, data schema descriptions, use of shared data dictionaries, license used, file format, and publisher support for interacting with data users.

Citation

CORSAR, D. and EDWARDS, P. 2017. Challenges of open data quality: more than just license, format, an customer support. Journal of data and information quality [online], 9(1), pages 1-4. Available from: https://doi.org/10.1145/3139489

• Journal Article Type: Article
• Acceptance Date: Jun 1, 2017
• Online Publication Date: Sep 15, 2017
• Publication Date: Mar 31, 2017
• Deposit Date: Apr 28, 2022
• Publicly Available Date: Apr 28, 2022
• Journal: Journal of Data and Information Quality
• Print ISSN: 1936-1955
• Electronic ISSN: 1936-1963
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 1
• Pages: 1-4
• DOI: https://doi.org/10.1145/3110291
• Keywords: Quality assurance; Applied computing; E-government; Open data
• Public URL: https://rgu-repository.worktribe.com/output/1580610