Organizations today have access to vast stores of data that come in a wide variety of forms and may be stored in places ranging from file cabinets to databases, and from library shelves to the Internet. The enormous growth in the quantity of data, however, has brought with it growing problems with the quality of information, further complicated by the struggles many organizations are experiencing as they try to improve their systems for knowledge management and organizational memory. Failure to manage information properly, or inaccurate data, costs businesses billions of dollars each year. This volume presents cutting-edge research on information quality. Part I seeks to understand how data can be measured and evaluated for quality. Part II deals with the problem of ensuring quality while processing data into information a company can use. Part III presents case studies, while Part IV explores organizational issues related to information quality. Part V addresses issues in information quality education.

Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online

What makes information useful? This seemingly simple and yet intriguing and complicated question is discussed in this book. It examines ways in which the quality of information can be improved in knowledge-intensive processes (such as on-line communication, strategy, product development, or consulting). Based on existing information quality literature, the book proposes a conceptual framework to manage information quality for knowledge-based content. It presents four proven principles to apply the framework to a variety of information products. Five in-depth company case studies show how information quality can be managed systematically. The book uses frequent diagrams and tables, as well as diagnostic questions and summary boxes to make its content actionable.

Data Quality begins with an explanation of what data is, how it is created and destroyed, then explores the true quality of data--accuracy, consistency and currentness. From there, the author covers the powerful methods of statistical quality control and process management to bear on the core processes that create, manipulate, use and store data values. Table of Contents: 1. Introduction; 2. Data and Information; 3. Dimensions of Data Quality; 4. Statistical Quality Control; 5. Process Management; 6. Process Representation and the Functions of Information Processing Approach; 7. Data Quality Requirements; 8. Measurement Systems and Data Quality; 9. Process Redesign Using Experimentation and Computer Simulation; 10. Managing Multiple Processes; 11. Perspective Prospects and Implications; 12. Summaries.

Technologies such as the Internet and mobile commerce bring with them ubiquitous connectivity, real-time access, and overwhelming volumes of data and information. The growth of data warehouses and communication and information technologies has increased the need for high information quality management in organizations. Information Quality Management: Theory and Applications provides solutions to information quality problems becoming increasingly prevalent.Information Quality Management: Theory and Applications provides insights and support for professionals and researchers working in the field of information and knowledge management, information quality, practitioners and managers of manufacturing, and service industries concerned with the management of information.

The book describes the most important quality management tools (e.g. QFD, Kano model), methods (e.g. FMEA, Six Sig-ma) and standards (e.g. IS0 9001, ISO 14001, ISO 27001, ISO 45001, SA8000). It reflects recent developments in the field. It is considered a must-read for students, academics, and practitioners.

Managing Quality, Fifth Edition is an essential resource for students and practitioners alike. This popular and highly successful introduction to Quality Management has been fully revised and updated to reflect recent developments in the field Includes new chapters on Improvement Approaches, Six Sigma, and new challenges in Quality Management Combines the latest information on the ISO 9000 quality management system series standards with up-to-date tools, techniques and quality systems Material has been re-ordered and changes to terminology have been made to bring the book completely up to date Provides a popular resource for students, academics, and business practitioners alike

Originally presented as the author's thesis (doctoral)--Universiteat Bern, 2010.

The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.

The 2nd edition of this book shows how the quality of information can be improved in such knowledge-intensive processes as on-line communication, strategy, product development, or consulting. The text offers proven principles for applying information quality management to a variety of information products. Case studies show how information quality management can increase satisfaction of knowledge workers and information consumers. Includes much new material.