Open data quality measurement framework: Definition and application to Open Government Data
By Antonio Vetro, Lorenzo Canova, Marco Torchiano, Camilo Orozco Minotas, Raimondo Iemma, Federico Morando. Government Information Quarterly, 2016
348 citations recorded by the source corpus.
Abstract The diffusion of Open Government Data (OGD) in recent years kept a very fast pace. However, evidence from practitioners shows that disclosing data without proper quality control may jeopardize dataset reuse and negatively affect civic participation. Current approaches to the problem in literature lack a comprehensive theoretical framework. Moreover, most of the evaluations concentrate on open data platforms, rather than on datasets. In this work, we address these two limitations and set up a framework of indicators to measure the quality of Open Government Data on a series of data quality dimensions at most granular level of measurement. We validated the evaluation framework by applying it to compare two cases of Italian OGD datasets: an internationally recognized good example of OGD, with centralized disclosure and extensive data quality controls, and samples of OGD from decentralized data disclosure (municipality level), with no possibility of extensive quality controls as in the former case, hence with supposed lower quality. Starting from measurements based on the quality framework, we were able to verify the difference in quality: the measures showed a few common acquired good practices and weaknesses, and a set of discriminating factors that pertain to the type of datasets and the overall approach. On the basis of this evaluation, we also provided technical and policy guidelines to overcome the weaknesses observed in the decentralized release policy, addressing specific quality aspects.
Subfields: Artificial Intelligence, Management Science and Operations Research, Political Science and International Relations
View Paper: https://www.semanticscholar.org/paper/3bde4151a2edbe217402a5d61e5a3eae08c11108
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Citations in the corpus
- Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities (2018, background): “However, quality varies as is demonstrated by Vetrò et al. (2016) by investigating two case studies.”