Corporate Data Quality
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Corporate Data Quality
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Author : Boris Otto
language : en
Publisher: epubli
Release Date : 2015-12-08
Corporate Data Quality written by Boris Otto and has been published by epubli this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-08 with Business & Economics categories.
Data is the foundation of the digital economy. Industry 4.0 and digital services are producing so far unknown quantities of data and make new business models possible. Under these circumstances, data quality has become the critical factor for success. This book presents a holistic approach for data quality management and presents ten case studies about this issue. It is intended for practitioners dealing with data quality management and data governance as well as for scientists. The book was written at the Competence Center Corporate Data Quality (CC CDQ) in close cooperation between researchers from the University of St. Gallen and Fraunhofer IML as well as many representatives from more than 20 major corporations. Chapter 1 introduces the role of data in the digitization of business and society and describes the most important business drivers for data quality. It presents the Framework for Corporate Data Quality Management and introduces essential terms and concepts. Chapter 2 presents practical, successful examples of the management of the quality of master data based on ten cases studies that were conducted by the CC CDQ. The case studies cover every aspect of the Framework for Corporate Data Quality Management. Chapter 3 describes selected tools for master data quality management. The three tools have been distinguished through their broad applicability (method for DQM strategy development and DQM maturity assessment) and their high level of innovation (Corporate Data League). Chapter 4 summarizes the essential factors for the successful management of the master data quality and provides a checklist of immediate measures that should be addressed immediately after the start of a data quality management project. This guarantees a quick start into the topic and provides initial recommendations for actions to be taken by project and line managers. Please also check out the book's homepage at cdq-book.org/
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
The Data Asset
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Author : Tony Fisher
language : en
Publisher: John Wiley & Sons
Release Date : 2009-06-22
The Data Asset written by Tony Fisher and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-22 with Business & Economics categories.
An indispensable guide that shows companies how to treat data as a strategic asset Organizations set their business strategy and direction based on information that is available to executives. The Data Asset provides guidance for not only building the business case for data quality and data governance, but also for developing methodologies and processes that will enable your organization to better treat its data as a strategic asset. Part of Wiley's SAS Business Series, this book looks at Business Case Building; Maturity Model and Organization Capabilities; 7-Step Programmatic Approach for Success; and Technologies Required for Effective Data Quality and Data Governance and, within these areas, covers Risk mitigation Cost control Revenue optimization Undisciplined and reactive organizations Proactive organizations Analysis, improvement, and control technology Whether you're a business manager or an IT professional, The Data Asset reveals the methodology and technology needed to approach successful data quality and data governance initiatives on an enterprise scale.
Executing Data Quality Projects
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Author : Danette McGilvray
language : en
Publisher: Elsevier
Release Date : 2008-09-01
Executing Data Quality Projects written by Danette McGilvray and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-01 with Computers categories.
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.
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
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Author : Jack E. Olson
language : en
Publisher: Elsevier
Release Date : 2003-01-09
Data Quality written by Jack E. Olson and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-09 with Computers categories.
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.
Data Quality
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Author : Prashanth Southekal
language : en
Publisher: John Wiley & Sons
Release Date : 2023-02-01
Data Quality written by Prashanth Southekal and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-01 with Computers categories.
Discover how to achieve business goals by relying on high-quality, robust data In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you’ll learn techniques to define and assess data quality, discover how to ensure that your firm’s data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications. The author shows you how to: Profile for data quality, including the appropriate techniques, criteria, and KPIs Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization. Formulate the reference architecture for data quality, including practical design patterns for remediating data quality Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Enterprise Knowledge Management
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Author : David Loshin
language : en
Publisher: Morgan Kaufmann
Release Date : 2001
Enterprise Knowledge Management written by David Loshin and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Business & Economics categories.
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.
Handbook Of Data Quality
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Author : Shazia Sadiq
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-08-13
Handbook Of Data Quality written by Shazia Sadiq 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 2013-08-13 with Computers categories.
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.
Competing With High Quality Data
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Author : Rajesh Jugulum
language : en
Publisher: John Wiley & Sons
Release Date : 2014-03-10
Competing With High Quality Data written by Rajesh Jugulum and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-10 with Business & Economics categories.
Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, butlow-quality data can actually put a company at a disadvantage. Tobe used effectively, data must accurately reflect the real-worldscenario it represents, and it must be in a form that is usable andaccessible. Quality data involves asking the right questions,targeting the correct parameters, and having an effective internalmanagement, organization, and access system. It must be relevant,complete, and correct, while falling in line with pervasiveregulatory oversight programs. Competing with High Quality Data: Concepts, Tools andTechniques for Building a Successful Approach to Data Qualitytakes a holistic approach to improving data quality, fromcollection to usage. Author Rajesh Jugulum is globally-recognizedas a major voice in the data quality arena, with high-levelbackgrounds in international corporate finance. In the book,Jugulum provides a roadmap to data quality innovation,covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of abusiness Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effectivedata gathering The book also contains real-world case studies to illustrate howcompanies across a broad range of sectors have employed dataquality systems, whether or not they succeeded, and what lessonswere learned. High-quality data increases value throughout theinformation supply chain, and the benefits extend to the client,employee, and shareholder. Competing with High Quality Data:Concepts, Tools and Techniques for Building a Successful Approachto Data Quality provides the information and guidance necessaryto formulate and activate an effective data quality plan today.