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Author: Larry P. English
Publisher: Wiley
ISBN: 9780470134474
Category : Computers
Languages : en
Pages : 0
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Book Description
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
Author: Larry P. English
Publisher: Wiley
ISBN: 9780470134474
Category : Computers
Languages : en
Pages : 0
Get Book Here
Book Description
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
Author: Ron S. Kenett
Publisher: John Wiley & Sons
ISBN: 1118874447
Category : Mathematics
Languages : en
Pages : 381
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Book Description
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.
Author: Danette McGilvray
Publisher: Elsevier
ISBN: 0080558399
Category : Computers
Languages : en
Pages : 353
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Book Description
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.
Author: Danette McGilvray
Publisher: Academic Press
ISBN: 0128180161
Category : Computers
Languages : en
Pages : 378
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Book Description
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
Author: John R. Talburt
Publisher: Elsevier
ISBN: 0123819733
Category : Computers
Languages : en
Pages : 254
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Book Description
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.
Author: Larry P. English
Publisher: Wiley
ISBN: 9781118082072
Category : Computers
Languages : en
Pages : 544
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Book Description
A comprehensive guide to quality improvement from the leading expert in information and data warehouse quality. Each year, companies lose millions as a result of inaccurate and missing data in their operational databases. This in turn corrupts data warehouses, causing them to fail. With information quality improvement and control systems, like the ones described in this book, your company can reduce costs and increase profits from quality information assets. Written by an internationally recognized expert in information quality improvement, Improving Data Warehouse and Business Information Quality arms you with a comprehensive set of tools and techniques for ensuring data quality both in source databases and the data warehouse. With the help of best-practices case studies, Larry English fills you in on: How and when to measure information quality. How to measure the business costs of poor quality information. How to select the right information quality tools for your environment. How to reengineer and cleanse data to improve the information product before it reaches your data warehouse. How to improve the information creation processes at the source. How to build quality controls into data warehouse processes. AUTHORBIO: Larry P. English is the leading international expert in the field of information and data warehouse quality. He is a columnist for Data Management Review and a featured speaker at numerous Data Warehousing Conferences. Larry chairs Information Quality Conferences held around the world.
Author: Kuan-Tsae Huang
Publisher: Prentice Hall
ISBN: 9780130101419
Category : Business
Languages : en
Pages : 0
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Book Description
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Author: Carlo Batini
Publisher: Springer
ISBN: 3319241060
Category : Computers
Languages : en
Pages : 520
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Book Description
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.
Author: Martin J. Eppler
Publisher: Springer Science & Business Media
ISBN: 3540322256
Category : Business & Economics
Languages : en
Pages : 408
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Book Description
The 2nd edition of this book shows how the quality of information can be improved in such knowledge-intensive processes as on-line communication, strategy, product development, or consulting. The text offers proven principles for applying information quality management to a variety of information products. Case studies show how information quality management can increase satisfaction of knowledge workers and information consumers. Includes much new material.
Author: Laura Sebastian-Coleman
Publisher: Newnes
ISBN: 0123977541
Category : Computers
Languages : en
Pages : 404
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Book Description
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
