Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Data Resource Quality
Download Data Resource Quality full books in PDF, epub, and Kindle. Read online Data Resource Quality ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Executing Data Quality Projects by : Danette McGilvray
Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Elsevier. This book was released on 2008-09-01 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information is currency. Recent 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. In this important and timely new book, Danette McGilvray presents her “Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations. * Includes numerous templates, detailed examples, and practical advice for executing every step of the “Ten Steps approach. * 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 planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.
Download or read book Data Quality written by Rupa Mahanti and published by Quality Press. This book was released on 2019-03-18 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Quality written by Carlo Batini and published by Springer Science & Business Media. This book was released on 2006-09-27 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
Book Synopsis Executing Data Quality Projects by : Danette McGilvray
Download or read book Executing Data Quality Projects written by Danette McGilvray and published by Academic Press. This book was released on 2021-05-27 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Data Quality written by Thomas C. Redman and published by Digital Press. This book was released on 2001 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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"
Book Synopsis Data Resource Quality by : Michael H. Brackett
Download or read book Data Resource Quality written by Michael H. Brackett and published by Addison-Wesley Professional. This book was released on 2000 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Covering both data architecture and data management issues, the book describes the impact of poor data practices, demonstrates more effective approaches, and reveals implementation pointers for quick results."--Jacket.
Book Synopsis Exploratory Data Mining and Data Cleaning by : Tamraparni Dasu
Download or read book Exploratory Data Mining and Data Cleaning written by Tamraparni Dasu and published by John Wiley & Sons. This book was released on 2003-08-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.
Book Synopsis The Practitioner's Guide to Data Quality Improvement by : David Loshin
Download or read book The Practitioner's Guide to Data Quality Improvement written by David Loshin and published by Elsevier. This book was released on 2010-11-22 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Foundations of Data Quality Management by : Wenfei Fan
Download or read book Foundations of Data Quality Management written by Wenfei Fan and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Resource Simplexity by : Michael H. Brackett
Download or read book Data Resource Simplexity written by Michael H. Brackett and published by . This book was released on 2011 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you fully understand all the data in your organization's data resource? Can you readily find and easily access the data you need to support your business activities? If you find multiple sets of the same data, can you readily determine which is the most current and correct? No? Then consider this book essential reading. It will help you develop a high quality data resource that supports business needs. Data Resource Simplexity explains how a data resource goes disparate, how to stop that trend toward disparity, and how to develop a high quality, comparate data resource. It explains how to stop the costly business impacts of disparate data. It explains both the architectural and the cultural aspects of developing a comparate data resource. It explains how to manage data as a critical resource equivalent to the other critical resources of an organization - finances, human resource, and real property. Drawing from his nearly five decades of data management experience, plus his leveraging of theories, concepts, principles, and techniques from disciplines as diverse as human dynamics, mathematics, physics, agriculture, chemistry, and biology, Michael Brackett shows how you can transform your organization's data resource into a trusted invaluable companion for both business and data management professionals. Chapter 1 reviews the trend toward rampant data resource disparity that exists in most public and private sector organizations today - why the data resource becomes complex. Chapter 2 introduces the basic concepts of planned data resource comparity - how to make the data resource elegant and simple. Chapter 3 presents the concepts, principles, and techniques of a Common Data Architecture within which all data in the organization are understood and managed. Chapters 4 through 8 present the five architectural aspects of data resource management. Chapter 4 explains the development of formal data names. Chapter 5 explains the development of comprehensive data definitions. Chapter 6 explains the development of proper data structures. Chapter 7 explains the development of precise data integrity rules. Chapter 8 explains the management of robust data documentation. Chapters 9 through 13 present the five cultural aspects of data resource management. Chapter 14 presents a summary explaining that development of a comparate data resource is a cultural choice of the organization and the need for a formal data resource management profession. From the Fore...
Book Synopsis Data Quality Assessment by : Arkady Maydanchik
Download or read book Data Quality Assessment written by Arkady Maydanchik and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners."
Book Synopsis Handbook of Data Quality by : Shazia Sadiq
Download or read book Handbook of Data Quality written by Shazia Sadiq and published by Springer Science & Business Media. This book was released on 2013-08-13 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Quality written by Jack E. Olson and published by Elsevier. This book was released on 2003-01-09 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality. * Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets.
Download or read book Data Driven written by Thomas C. Redman and published by Harvard Business Press. This book was released on 2008-09-22 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that.
Book Synopsis Measuring Data Quality for Ongoing Improvement by : Laura Sebastian-Coleman
Download or read book Measuring Data Quality for Ongoing Improvement written by Laura Sebastian-Coleman and published by Newnes. This book was released on 2012-12-31 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges Enables discussions between business and IT with a non-technical vocabulary for data quality measurement Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation
Download or read book Data Quality written by Richard Y. Wang and published by . This book was released on 2014-01-15 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Quality for the Information Age by : Thomas C. Redman
Download or read book Data Quality for the Information Age written by Thomas C. Redman and published by Artech House Publishers. This book was released on 1996 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
