Foundations Of Data Quality Management
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Foundations Of Data Quality Management
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Author : Paul Thomas
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-06-14
Foundations Of Data Quality Management written by Paul Thomas and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-14 with categories.
A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules.
Foundations Of Data Quality Management
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Author : Wenfei Fan
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Foundations Of Data Quality Management written by Wenfei Fan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues
Data Quality
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Author : Richard Y. Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11
Data Quality written by Richard Y. Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-11 with Computers categories.
Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.
Defining The Dimensions Of Data Quality
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Author : Lisa Marie Guarascio
language : en
Publisher:
Release Date : 1991
Defining The Dimensions Of Data Quality written by Lisa Marie Guarascio and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Foundations Of Data Quality Management
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Author : Wenfei Fan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2012
Foundations Of Data Quality Management written by Wenfei Fan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
Provides an overview of fundamental issues underlying central aspects of data quality - data consistency, data deduplication, data accuracy, data currency, and information completeness. The book promotes a uniform logical framework for dealing with these issues, based on data quality rules.
Data Quality
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Author : Thomas C. Redman
language : en
Publisher: Digital Press
Release Date : 2001
Data Quality written by Thomas C. Redman and has been published by Digital Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
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"
Data Quality Management
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Author : Henry B. Kon
language : en
Publisher:
Release Date : 1996
Data Quality Management written by Henry B. Kon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
Executing Data Quality Projects
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Author : Danette McGilvray
language : en
Publisher: Academic Press
Release Date : 2021-05-27
Executing Data Quality Projects written by Danette McGilvray and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-27 with Computers categories.
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
Data Quality In The Age Of Ai
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Author : Andrew Jones
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-24
Data Quality In The Age Of Ai written by Andrew Jones and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-24 with Computers categories.
Unlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric culture Key Features Gain a profound understanding of the interplay between data quality and AI Explore strategies to improve data quality with practical implementation and real-world results Acquire the skills to measure and evaluate data quality, empowering data-driven decisions Purchase of the Kindle book includes a free PDF eBook Book DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learn Discover actionable steps to establish data quality as the foundation of your data culture Enhance data quality directly at its source with effective strategies and best practices Elevate data quality standards and enhance data literacy within your organization Identify and measure data quality within the dataset Adopt a product mindset to address data quality challenges Explore emerging architectural patterns like data mesh and data contracts Assign roles, responsibilities, and incentives for data generators Gain insights from real-world case studies Who this book is for This report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Data Quality
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Author : Thomas C. Redman
language : en
Publisher: Random House Puzzles & Games
Release Date : 1992
Data Quality written by Thomas C. Redman and has been published by Random House Puzzles & Games this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
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.
