Editorial: Special Issue on Human in the Loop Data Curation

This Special Issue of the Journal of Data and Information Quality (JDIQ) contains novel theoretical and methodological contributions on data curation involving humans in the loop. In this editorial, we summarize the scope of the issue and briefly describe its content.

Dimensions of data sparseness and their effect on supply chain visibility

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of...

IFC models for semi-automating common planning checks for building permits

To support building permit issuing with automatic digital tools, the reuse of models produced by designers would make the process quicker and more objective. However, current studies and pilots often leave a gap with respect to the models as actually provided by architects, having varying quality and content. In this study, rather than taking...

Using Business Data in Customs Risk Management: Data Quality and Data Value Perspective

With the rise of data analytics use in government, government organizations are starting to explore the possibilities of using business data to create further public value. This process, however, is far from straightforward: key questions that governments need to address relate to the quality of this external data and the value it brings. In the...

Trusted decision-making: Data governance for creating trust in data science decision outcomes

Organizations are increasingly introducing data science initiatives to support decision-making. However, the decision outcomes of data science initiatives are not always used or adopted by decision-makers, often due to uncertainty about the quality of data input. It is, therefore, not surprising that organizations are increasingly turning to...

Citizens’ Trust in Open Government Data: A Quantitative Study about the Effects of Data Quality, System Quality and Service Quality

Previous research assumes that poor quality of Open Government Data (OGD), OGD portals, and the services provided for OGD may result in reduced trust of citizens in OGD. However, studies that empirically test this assumption are scarce. Using the Information Systems (IS) Success Model as a theoretical basis, this study aims to examine the...

Data Governance as Success Factor for Data Science

More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures...

val3dity: validation of 3D GIS primitives according to the international standards

The validity of 3D primitives in 3D GIS datasets is often a prerequisite for using them in simulation and decision-making software, e.g. visibility analysis, noise pollution assessment, and energy estimation. However, while agreed definitions exist (in the international standard ISO19107), most software vendors ignore them and implement simpler...

A Process Pattern Model for Tackling and Improving Big Data Quality

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality....

Relating big data and data quality in financial service organizations

Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex relationship between big data and data quality. We do this by comparing the big data characteristics with...

Solving the horizontal conflation problem with a constrained Delaunay triangulation

Datasets produced by different countries or organisations are seldom properly aligned and contain several discrepancies (e.g., gaps and overlaps). This problem has been so far almost exclusively tackled by snapping vertices based on a user-defined threshold. However, as we argue in this paper, this leads to invalid geometries, is error-prone,...