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This work fulfills the need for a conceptual and technical framework to improve understanding of Information Quality (IQ) and Information Quality standards. The meaning and practical implementation of IQ are addressed, as it is relevant to any field where there is a need to handle data and issues such as accessibility, accuracy, completeness, currency, integrity, reliability, timeliness, usability, the role of metrics and so forth are all a part of Information Quality. In order to support the cross-fertilization of theory and practice, the latest research is presented in this book. The perspectives of experts from beyond the origins of IQ in computer science are included: library and information science practitioners and academics, philosophers of information, of engineering and technology, and of science are all contributors to this volume. The chapters in this volume are based on the work of a collaborative research project involving the Arts and Humanities Research Council and Google and led by Professor Luciano Floridi, University of Oxford. This work will be of interest to anyone handling data, including those from commercial, public, governmental and academic organizations. The expert editors’ contributions introduce issues of interest to scientists, database curators and philosophers, even though the issues may be disguised in the language and examples common to a different discipline.
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.
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain. Presents a framework for integrating domain knowledge with data analysis. Provides a combination of both methodological and practical aspects of data analysis. Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects. Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys. Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website. This book will be beneficial for researchers in academia and in industry, analysts, consultants, and agencies that collect and analyse data as well as undergraduate and postgraduate courses involving data analysis.
How to apply data quality management techniques to marketing, sales, and other specific business units Author and information quality management expert Larry English returns with a sequel to his much-acclaimed book, Improving Data Warehouse and Business Information Quality. In this new book he takes a hands-on approach, showing how to apply the concepts outlined in the first book to specific business areas like marketing, sales, finance, and human resources. The book presents real-world scenarios so you can see how to meld data quality concepts to specific business areas such as supply chain management, product and service development, customer care, and others. Step-by-step instruction, practical techniques, and helpful templates from the author help you immediately apply best practices and start modeling your own quality initiatives. Maintaining the quality and accuracy of business data is crucial; database managers are in need of specific guidance for data quality management in all key business areas Information Quality Applied offers IT, database, and business managers step-by-step instruction in setting up methodical and effective procedures The book provides specifics if you have to manage data quality in marketing, sales, customer care, supply chain management, product and service management, human resources, or finance The author includes templates that readers can put to immedate use for modeling their own quality initiatives A Companion Web site provides templates, updates to the book, and links to related sites
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.
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.
Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality. It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ). The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology. It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the Algebraic Model for Entity Resolution, which are the major theoretical models that support Entity Resolution. In relation to this, the book briefly discusses entity-based data integration (EBDI) and its model, which serve as an extension of the Algebraic Model for Entity Resolution. There is also an explanation of how the three commercial ER systems operate and a description of the non-commercial open-source system known as OYSTER. The book concludes by discussing trends in entity resolution research and practice. Students taking IT courses and IT professionals will find this book invaluable. - First authoritative reference explaining entity resolution and how to use it effectively - Provides practical system design advice to help you get a competitive advantage - Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.
Drawing on IBM's unsurpassed technical communications experience, readers discover today's best practices for meeting nine quality characteristics: accuracy, clarity, completeness, concreteness, organization, retrievability, style, task orientation, and visual effectiveness. Packed with guidelines, checklists, and before-and-after examples, Developing Quality Technical Information, Third Edition is an indispensable resource for the future of technical communication.
"Incorrect and misleading information associated with an enterprise's production and service jeopardize both customer relationships and customer satisfaction, and ultimately have a negative effect on revenue. This book provides insight and support for academic professionals as well as for practitioners concerned with the management of information"--Provided by publisher.
The Internet and the World Wide Web (WWW) are becoming more and more important in our highly interconnected world as more and more data and information is made available for online access. Many individuals and governmental, commercial, cultural, and scientific organizations increasingly depend on information sources that can be accessed and queried over the Web. For example, accessing flight schedules or retrieving stock information has become common practice in todays world. When accessing this data, many people assume that the information accessed is accurate and that the data source can be accessed reliably. These two examples clearly demonstrate that not only the information content is important, the information about the quality of the data becomes an even more crucial and critical aspect for individuals and organizations when they make plans or take decisions based on the results of their queries. More precisely, having access to information of known quality becomes critical for the well-being and indeed for the functioning of modern industrialized societies. Surprisingly, despite the urgent need for clear concepts and techniques to judge and value quality and for technology to use such (meta) information, very few scientific results are known and available. Few approaches are known to use quality measures for accessing and querying information over the Web. Only a limited number of products on the IT market address this burning problem.