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.
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.
"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.
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
An essential quality management resource for students and practitioners alike—now in its sixth edition This popular and highly successful text on Quality Management has been fully revised and updated to reflect recent developments in the field. New to the Sixth Edition is timely coverage of agile development, emerging markets, product research, evidence based decision-making, and quality control. Some of the material has been re-ordered and changes to terminology have been made to bring the book completely up to date. Contributions from new co-author David Bamford offer insights from a veteran teacher and practitioner. A popular resource for students, academics, and business practitioners alike Combines the latest information on quality management system series standards with up-to-date tools, techniques and quality systems Includes insights on quality, operations management, and strategic process improvement Highly relevant for professionals, particularly those involved with reacting to rapid developments in the global market The word "quality" has many definitions, dependent on context and situation. It is often over-used but always in-demand, and it can make or break a business. Quality management is becoming an increasingly vital factor in the success of a product or service, and it requires constant attention and a continuous drive to do better. Managing Quality is a comprehensive resource that helps you ensure – and sustain – high quality standards.
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.
This is not the kind of book that youll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph.D., the Data Doc Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.
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.
This volume presents a methodology for defining, measuring and improving data quality. It lays out an economic framework for understanding the value of data quality, then outlines data quality rules and domain- and mapping-based approaches to consolidating enterprise knowledge.
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.
