Open Data Quality

TL;DR

This paper presents a method for assessing open data quality through a specification-based approach, enabling error detection and evaluation to ensure trustworthiness for decision-making.

Contribution

It introduces a novel data quality specification framework tailored for open data, facilitating systematic quality evaluation and error identification.

Findings

Applied to multiple open datasets, demonstrating effectiveness in quality assessment.

Identified common data quality issues in open datasets, highlighting the need for systematic evaluation.

Showed that the approach can improve trust in open data for stakeholders.

Abstract

The research discusses how (open) data quality could be described, what should be considered developing a data quality management solution and how it could be applied to open data to check its quality. The proposed approach focuses on development of data quality specification which can be executed to get data quality evaluation results, find errors in data and possible problems which must be solved. The proposed approach is applied to several open data sets to evaluate their quality. Open data is very popular, free available for every stakeholder - it is often used to make business decisions. It is important to be sure that this data is trustable and error-free as its quality problems can lead to huge losses.