Prologue: Research and Practice in Data Quality Management

Australasian Database Conference, 2011

Data Quality is a cross-disciplinary and often domain specific problem due to the importance of fitness for use in the definition of data quality metrics. It has been the target of research and development for over 4 decades by business analysts, solution architects, database experts and statisticians to name a few. However, the changing landscape of data quality challenges indicate the need for holistic solutions. As a first step towards bridging any gaps between the various research communities, we undertook a comprehensive literature study of data quality research published in the last two decades 1 . In this study we considered a broad range of Information System (IS) and Computer Science (CS) publication (conference and journal) outlets. The main aims of the study were to understand the current landscape of data quality research, to create better awareness of (lack of) synergies between various research communities, and, subsequently, to direct attention towards holistic solutions. In this paper, we present a summary of the findings from the study, that include a taxonomy of data quality problems, identification of the top themes, outlets and main trends in data quality research, as well as a detailed thematic analysis that outlines the overlaps and distinctions between the focus of IS and CS publications.

IEEE Transactions on Knowledge and Data Engineering, 1995

Abstiuct-Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided.

International Journal of Business Information Systems, 2016

Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.

MIT International Conference on Information Quality, 2011

Research and practice in data and information quality is characterized by methodological as well as topical diversity. The cross-disciplinary nature of data quality problems as well as a strong focus on solutions based on the fitness for use principle has further diversified the body of knowledge on data and information quality. Although research pluralism is highly warranted, there is evidence that substantial developments in the past have been isolationist. As data quality increases in importance and complexity, there is a need to motivate exploitation of synergies across diverse research communities in order to form holistic solutions that span across organizational, architectural and computational aspects of data quality management. As a first step towards bridging gaps between the various communities, we undertook a literature review of data quality research published in a range of Information System (IS) and Computer Science (CS) publication outlets, and conducted a global survey of data quality management practitioners. In this paper, we present taxonomy of the main research topics contrasted against industry perceptions on the relative importance of those topics. Through the research-industry contrast, we hope to create a better understanding of research industry synergies as well as highlighting areas of high potential gaps and impact for the research community.

Journal of Theoretical and Applied Information Technology , 2019

Data quality drawn a major concern when dealing with data especially in the event that insightful outputs is needed. Research in data quality emerged in various topics and diversification in known knowledge and used approach is inevitable. In this paper, we apply systematic review study to explain the landscape of data quality and to identify available research gap by using categorization and mapping. Our search scope is limited to research articles from journals, conference proceedings and magazine published between 2010 until 2016. We defined three types of main categorization to map the selected research articles and to answer our research questions. These categorization focus on research topics, research type and contribution type. On average, fifty-four research articles related to data quality were published every year. This number shows the importance of data quality research in various research topics such as online users, database, web information, sensors and big data. This study also indicates that almost half of the selected articles proposed a novel solution or an essential extension of an existing data quality technique. Moreover, most of the selected research articles belongs to the model type in the contribution category. Our mapping also suggests that obvious contribution disparity happen between contribution in metric type and model type category.

2015

Data quality (DQ) has been studied in significant depth over the last two decades and has received attention from both the academic and the practitioner community. Over that period of time a large number of data quality dimensions have been identified in due course of research and practice. While it is important to embrace the diversity of views of data quality, it is equally important for the data quality research and practitioner community to be united in the consistent interpretation of this foundational concept. In this paper, we provide a step towards this consistent interpretation. Through a systematic review of research and practitioner literature, we identify previously published data quality dimensions and embark on the analysis and consolidation of the overlapping and inconsistent definitions. We stipulate that the shared understanding facilitated by this consolidation is a necessary prelude to generic and declarative forms of requirements modeling for data quality.

Annual Review of Statistics and Its Application

Data, and hence data quality, transcend all boundaries of science, commerce, engineering, medicine, public health, and policy. Data quality has historically been addressed by controlling the measurement processes, controlling the data collection processes, and through data ownership. For many data sources being leveraged into data science, this approach to data quality may be challenged. To understand that challenge, a historical and disciplinary perspective on data quality, highlighting the evolution and convergence of data concepts and applications, is presented.

Journal of Theoretical and Applied Information Technology , 2017

The aim of this review is to highlight issues in data quality research and to discuss potential research opportunity to achieve high data quality within an organization. The review adopted systematic literature review method based on research articles published in journals and conference proceedings. We developed a review strategy based on specific themes such as current research area in data quality, critical dimensions in data quality, data quality management model and methodologies and data quality assessment methods. Based on the review strategy, we select relevant research articles, extract and synthesis the information to answer our research questions. The review highlights the advancement of data quality research to resemble its real world application and discuss the available gap for future research. Research area such as organizations management, data quality impact towards the organization and database related technical solutions for data quality dominated the early years of data quality research. However, since the Internet is now taking place as the new information source, the emerging of new research areas such as data quality assessment for web and big data is inevitable. This review also identifies and discusses critical data quality dimensions in organization such as data completeness, consistency, accuracy and timeliness. We also compare and highlight gaps in data quality management model and methodologies. Existing model and methodologies capabilities are restricted to the structured data type and limit its ability to assess data quality in web and big data. Finally, we uncover available methods in data quality assessment and highlight its limitation for future research. This review is important to highlight and analyse limitation of existing data quality research related to the recent needs in data quality such as unstructured data type and big data.

… Workshop on Data and …, 2005

Abstract. In today's society the exploration of one or more databases to extract information or knowledge to support management is a critical success factor for an organization. However, it is well known that several problems can affect data quality. These problems have a ...

2012

Data quality survey results 2012