What modes and domains of knowledge about data production processes are most critical for producing high-quality data? This study provides an answer to this question. Data are collected via questionnaire and analyzed using linear regression. The results show some similarities and differences in which knowledge variables are significant for various data quality dimensions. Three results are of p...

This paper describes a Multilingual Open Data corpus for European languages that was built in scope of the MODEL project. We describe the approach chosen to select data sources, which data sources were used, how the source data was handled, what tools were used and what data was obtained in the result of the project. Obtained data quality is presented, and a summary of challenges and chosen sol...

GIS professionals seem to assume that better data leads to better decisions. Is this true? An analysis to determine the effects of data quality on the quality of decisions reveals relations that are useful to consider before blindly investing in data quality improvement. This article analyzes data quality and how it influences the quality of a decision with an example of environmental engineeri...

Many information quality initiatives and projects need to demonstrate the potential benefits of their IQrelated activities already in their planning stage. In doing so, practitioners rely on cost estimates based on current non-quality data effects (that are then compared to data quality improvement costs). In producing such estimates on costs caused by low quality data, it is difficult to ident...

Currently, information has become an increasingly important production factor which has led to the emergence of data marketplaces that leverage big data technologies. However, value attribution of data is still difficult. This work suggests to discuss what role data quality can play in this context, particularly: what quality measures are relevant in the context of big data, how they can be mea...

Organizations in various research, military, commercial, and governmental institutions have realized the importance of systems integration. As information systems are integrated and information highways established, quality of data flowing through these highways to data consumers becomes increasingly critical. A key, and largely unexplored, challenge lies in how to evaluate data quality. This p...

The value of management decisions, the security of our nation, and the very foundations of our business integrity are all dependent on the quality of data and information. However, the quality of the data and information is dependent on how that data or information will be used. This paper proposes a theory of data quality based on the five principles defined by J. M. Juran for product and serv...

Presented is the Combined Conceptual Life Cycle (CCLC) Model of Information Quality (IQ). The CCLC conceptualises information/data quality as being a highly relative construct, best understood in terms of sixteen IQ dimensions housed within four broad IQ categories: namely; Intrinsic IQ, Representational IQ, Interactional IQ, and Contextual IQ. The four categories themselves are seen as falling...

Data quality is commonly defined as fitness for use. The problem of identifying the quality of data is faced by many data consumers. To make the task of finding good quality datasets more efficient, we introduce the Dataset Quality Ontology (daQ). The daQ is a lightweight, extensible vocabulary for attaching the results of quality benchmarking of a linked open dataset to that dataset. We discus...