Data quality

The Central Statistics Agency (BPS) is a government institution that has the authority to carry out statistical activities in the form of censuses and surveys, to produce statistical data needed by the government, the private sector and the general public, as a reference in planning, monitoring, and evaluation of development results. Therefore, providing quality statistical data is very decisive because it will have an impact on the effectiveness of decision making. This paper aims to develop a framework to determine priority of solutions in overcoming data quality problems using the Analytic Hierarchy Process (AHP). The framework is built by conducting interviews and Focus Group Discussion (FGD) on experts to get the interrelationship between problems and solutions. The model that has been built is then tested in a case study, namely the Central Jakarta Central Bureau of Statistics (BPS). The results of the study indicate that the proposed model can be used to formulate solutions to data problems in BPS.

Over the last 20 years, and particularly with the advent of Big Data and analytics, the research area around Data and Information Quality (DIQ) is still a fast growing research area. There are many views and streams in DIQ research, generally aiming at improving the effectiveness of decision making in organizations. Although there are a lot of researches aimed at clarifying the role of BIG data quality for organizations, there is no comprehensive literature review that shows the main differences between traditional data quality researches and Big Data quality researches. This paper analyzed the papers published in Big data quality and find out that there is almost no new mainstream about Big Data quality. It is shown in this paper that the main concepts of data quality does not changes in Big Data context and that only some new issues have been added to this area.

Data is considered the most crucial element in open banking processes and services. Therefore, it is necessary to pay attention to various aspects of the quality of this data in order to provide appropriate and expected services to customers. In this research, various dimensions representing different aspects of data quality were investigated in the field of open banking. This research has been conducted in two main steps: The Delphi method and the pairwise comparisons method. In the first step, various dimensions of data quality in open banking were extracted based on the Delphi method. In the next step, the importance of each of these dimensions was assessed relative to each other using the pairwise comparisons method, and the most crucial dimensions were determined. Based on the results obtained from these two methods, the significance of eleven dimensions of data quality in this field was determined. The best overall weighted averages were related to dimensions such as accuracy, accessibility, relevancy, timeliness, consistency, security, interpretability, reputation, believability, ease of understanding, and value-added, respectively. Banks and fintech companies offering open banking services can consider these dimensions when evaluating the quality of their data to enhance the provision of superior services

Decision-making processes are significantly influenced by a myriad of factors, with data quality emerging as a crucial determinant. Despite widespread awareness of the detrimental effects of poor-quality data on decisions, organizations struggle with persistent challenges because of the sheer volume of data within their systems. Existing literature advocates for providing Data Quality Metadata (DQM) to help decision-makers communicate data quality levels. However, concerns about potential cognitive overload induced by DQM may hinder decision-makers and negatively impact outcomes. To address this concern, we conducted an experimental exploration into the impact of Data Quality Management (DQM) on decision outcomes. Our study aimed to identify specific groups of decision-makers benefiting from DQM and uncover factors influencing its usage. Statistical analyses revealed that decision-makers with a heightened awareness of data quality demonstrated improved Data Quality Management (DQM) utilization, leading to increased decision accuracy. Nevertheless, a trade-off was observed as the efficiency of decision-makers suffered when employing Decision Quality Management (DQM). We propose that the positive impact of incorporating Data Quality Management (DQM) on decision outcomes is contingent on characteristics such as a high level of knowledge about data quality. However, we acknowledge that the inference of this positive impact could be more transparent and thoroughly explained. Our findings caution against a blanket inclusion of Data Quality Management (DQM) in data warehouses, emphasizing the need for tailored investigations into its utility and impact within specific organizational settings.

Organizing a large volume of scientific multimedia data requires the use of appropriate indexing methods as one of the processes of information organization. Appropriate methods and algorithms are those that lead to the improvement of various aspects of quality in the process of organizing and retrieving information. For this reason, the purpose of this research is to identify the most important dimensions of data quality in the field of scientific multimedia indexing. In order to achieve this goal, a comparison of different dimensions of data quality has been made based on different criteria and the most important dimensions have been identified using Shannon entropy weighting approach and TOPSIS group ranking method. Also, using the correlation matrix, the intensity and direction of the relationship and correlation between the different dimensions of data quality have been evaluated. Based on the results of the first part of the research, the best ranks (priorities) were related to the data quality dimensions of recall, precision, completeness, appropriate amount of data, accuracy, relevancy, concise 1, consistency, concise 2, interpretability, value-added and accessibility, respectively. The results obtained from the second part of the research showed that the data quality dimensions of interpretability and relevancy had the highest correlation with the most important dimensions, i.e. recall and precision. As one of the implications of this research, it is possible to consider the measurement and evaluation of scientific multimedia data indexing methods based on different aspects of data quality and their importance.