Classification of Open Government Data Solutions’ Help: A Novel Taxonomy and Cluster Analysis

Abstract

Open Government Data (OGD) pose that public organisations should freely share data for anyone to reuse without restrictions. However, the rawness of this data proves to be a challenge for data or information seekers. OGD-based solutions, such as interactive maps and dashboards, could help seekers overcome this difficulty and use OGD to satisfy needs, helping them to work effectively, solve problems, or pursue hobbies. However, there are several challenges that need to be considered when designing solutions, such as seekers wanting to solve problems rather than consuming information and aiming for quick wins over quality. Previous research has classified OGD solutions, focusing on general concepts. The next step is to reveal helpful patterns in OGD solutions, helping seekers. This paper presents a taxonomy with 24 criteria to classify these patterns. It was tested on 40 OGD solutions, and the resulting classifications were grouped in a cluster analysis, identifying 16 key criteria and 6 clusters. The clusters are (1) simple-personalised, (2) proactive multi-visual, (3) lightly-facilitated exploration, (4) facilitated data-management, (5) facilitated information exploration, and (6) horizon solutions. One unexpected finding is that helpful patterns do not cluster following themes, types, or purposes of solutions. Another finding is that the importance of key criteria varies between the clusters.