​Christoph Samitsch investigates whether decision-making efficiency is being influenced by the quality of data and information. Results of the research provide evidence that defined data quality dimensions have an effect on decision-making performance as well as the time it takes to make a decision.

Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality. Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it. The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points. Allows readers to go directly to the topic of interest Provides web-based material so readers can cut and paste figures and tables into documents within their organizations Gives step-by-step instructions for applying most techniques and summarizes what "works"

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

All aspects of data management are explored in this title, which provides detailed analyses of quality problems and their impacts, potential solutions and how they are combined to form an overall data quality program, senior management's role, and methods used to make and sustain improvements.

This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.

Abstract: The quality of the data used in a decision process has a direct impact on the quality of the decisions. Unfortunately, data quality (DQ) issues are inherent in information systems. Quality metadata is data that describes the quality of the data. It can inform decision makers about the quality of the data used in the decision process and can potentially enhance decision performance. While research has shown that quality metadata can change decision outcomes, it has not shown whether it improves decision performance. This dissertation first investigates whether and how DQ metadata improves decision performance in structured decision environments. When the DQ metadata is available, integrating it into the decision process requires allocating extra information processing capacity to it. This may impact overall decision performance differently, subject to other factors like task complexity and decision makers' experience. Using a set of experiments, we explore those important relationships. The findings support our general proposition that when there is sufficient amount of cognitive capacity available, the provision of DQ metadata will improve the overall quality of decision-making. However, when the complexity of the decision task increases, thereby reducing the cognitive capacity that the decision-maker has to spare, the provision of DQ metadata can negatively affect decision performance. This research further investigates the use of data visualization to reduce this cognitive load. Building upon the findings from the first part, we develop a prototype decision support system that utilizes visualization to reduce the cognitive effort in integrating quality metadata into the decision process. Using an experimental setup, we assess the impact of data visualization on decision performance using a common type of decision task, structured choice. The results confirm the benefits of visualization for integrating DQ metadata into the decision process. This research has important implications for decision-making, data management and decision support systems. The findings can help (a) identify the conditions under which the provision of DQ metadata is beneficial for decision making, (b) justify the need of including DQ metadata in data management, and (c) shed light on designing decision support systems that integrate DQ metadata for effective decision-making.

In an effort to increase knowledge and understanding of the process of assuring data quality and validity in clinical trials, the IOM hosted a workshop to open a dialogue on the process to identify and discuss issues of mutual concern among industry, regulators, payers, and consumers. The presenters and panelists together developed strategies that could be used to address the issues that were identified. This IOM report of the workshop summarizes the present status and highlights possible strategies for making improvements to the education of interested and affected parties as well as facilitating future planning.