The purpose of this research note is to outline data quality issues in open data sets and set an agenda for future research to address this risk to benefit from open data investments. Data and information obtained from data analysis are an important asset for the construction and maintenance of information systems. Since data is an essential resource, data quality is critical to improving data quality and improving the efficiency of business processes. The relationships between the four key data quality parameters for process improvement are often overlooked…
From a tactical perspective, organizations need to identify and evaluate areas of data quality improvement and carry out data quality improvement projects to correct and prevent future problems. for enterprises with poor IT infrastructure, where the quality of data depends heavily on the tasks performed by humans. This study aims to develop and evaluate a new method for structured data that is simple and practical so that it can be easily applied in real world situations. The new method detects potentially dangerous tasks in the process and adds new improvement tasks to counter them..
To achieve continuous improvement, a reward system has also been developed to help you better select proposed improvement objectives. The DQ task-based approach is most suitable for small and medium-sized organizations, and its ease of implementation is one of its most salient features. The example shows that TBDQ is effective in choosing the optimal actions to improve DQ in terms of cost and improvement. Open data aims to unleash the innovative potential of enterprises, governments and entrepreneurs, but they also present significant challenges to their effective use. While there have been numerous advances in innovation based on the open data paradigm, there is uncertainty about the data quality of such data sets…
By allowing enterprises to localize and establish data exceptions, data teams can begin to develop a strategy for rehabilitation processes. Delivering reliable data on time is the ultimate goal of any company. No company will ever question the need for quality information. The main challenge is to create a model that can minimize the amount of inaccurate data entered and effectively correct any existing inaccurate data. Data quality requires strategic and tactical orientation, in addition to close cooperation in business and information technology. Strategically, a company needs to start by creating a culture of quality. Data quality is everyone’s job, but it is management that needs to convey that perspective and, most importantly, create the right framework and channels to help people succeed….
The purpose of this review is to highlight data quality research issues and to discuss the research potential for achieving high data quality in an organization. The review used a method of systematic literature review based on scientific articles published in journals and conference papers. We have developed a review strategy based on specific topics such as the current data quality research area, critical aspects of data quality, data quality management model and methodologies, and methods for evaluating data quality. data. Based on the review strategy, we select relevant research articles, extract and synthesize information to answer our research questions. The review highlights the progress of data quality research to likely current applications and discusses a gap for future research. The first years of data quality research were dominated by research areas such as organization management, the impact of data quality on organizations, and technical solutions related to databases….
This uncertainty in data quality poses a threat to the value that can be derived from such data. Data quality has been extensively studied for many decades and many approaches to data quality management have been proposed. However, these approaches are usually based on internal datasets for organizations with knowledge of metadata and the domain of data semantics. On the other hand, open data is often unknown to users and may not have metadata..
However, as the Internet is now emerging as a new source of information, new areas of research such as data quality assessment for the Internet and big data are inevitable. This review also identifies and discusses critical dimensions of data quality in an organization, such as integrity, consistency, accuracy, and timeliness….
We also compare and highlight gaps in the data quality management model and methodologies. The capabilities of existing models and methodologies are limited by the structured type of data and limit their ability to measure the quality of online data and big data. Finally, we discover the available methods for evaluating data quality and highlight their limitations for future research. This review is important for identifying and analyzing the limitations of existing data quality studies in relation to recent data quality needs, such as unstructured data types and big data. Reporting and monitoring are ROI key factors in data quality management as they provide real-time visibility of data health at all times..
