Executing Data Quality Projects

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

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  • Author : Danette McGilvray
  • Publisher : Academic Press
  • Pages : 376 pages
  • ISBN : 0128180161
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKExecuting Data Quality Projects

Executing Data Quality Projects

Executing Data Quality Projects
  • Author : Danette McGilvray
  • Publisher : Academic Press
  • Release : 27 May 2021
GET THIS BOOKExecuting Data Quality Projects

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

Executing Data Quality Projects

Executing Data Quality Projects
  • Author : Danette McGilvray
  • Publisher : Elsevier
  • Release : 01 September 2008
GET THIS BOOKExecuting Data Quality Projects

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

The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement
  • Author : David Loshin
  • Publisher : Elsevier
  • Release : 22 November 2010
GET THIS BOOKThe Practitioner's Guide to Data Quality Improvement

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

Data Quality Assessment

Data Quality Assessment
  • Author : Arkady Maydanchik
  • Publisher : Technics Publications
  • Release : 01 April 2007
GET THIS BOOKData Quality Assessment

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

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
  • Author : Laura Sebastian-Coleman
  • Publisher : Newnes
  • Release : 31 December 2012
GET THIS BOOKMeasuring Data Quality for Ongoing Improvement

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

Data Quality

Data Quality
  • Author : Jack E. Olson
  • Publisher : Elsevier
  • Release : 09 January 2003
GET THIS BOOKData Quality

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.

A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Seventh Edition and The Standard for Project Management (RUSSIAN)

A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Seventh Edition and The Standard for Project Management (RUSSIAN)
  • Author : Project Management Institute Project Management Institute
  • Publisher : Project Management Institute
  • Release : 01 August 2021
GET THIS BOOKA Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Seventh Edition and The Standard for Project Management (RUSSIAN)

PMBOK&® Guide is the go-to resource for project management practitioners. The project management profession has significantly evolved due to emerging technology, new approaches and rapid market changes. Reflecting this evolution, The Standard for Project Management enumerates 12 principles of project management and the PMBOK&® Guide &– Seventh Edition is structured around eight project performance domains.This edition is designed to address practitioners' current and future needs and to help them be more proactive, innovative and nimble in enabling desired project outcomes.This

Journey to Data Quality

Journey to Data Quality
  • Author : Yang W. Lee,Leo L. Pipino,Richard Y. Wang,James D. Funk
  • Publisher : Mit Press
  • Release : 24 May 2022
GET THIS BOOKJourney to Data Quality

A guide for assessing an organization's data quality practice and a roadmap for implementing a viable data and information quality management program, based on rigorous research and drawing on real-world examples. All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems leve--installing the latest software or developing an expensive data warehouse--solve the basic problem of bad data quality practices. Journey to Data Quality offers a roadmap that can be used

Data Quality

Data Quality
  • Author : Rupa Mahanti
  • Publisher : Quality Press
  • Release : 18 March 2019
GET THIS BOOKData Quality

“This is not the kind of book that you’ll 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

Handbook of Data Quality

Handbook of Data Quality
  • Author : Shazia Sadiq
  • Publisher : Springer Science & Business Media
  • Release : 13 August 2013
GET THIS BOOKHandbook of Data Quality

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

Making Enterprise Information Management (EIM) Work for Business

Making Enterprise Information Management (EIM) Work for Business
  • Author : John Ladley
  • Publisher : Morgan Kaufmann
  • Release : 03 July 2010
GET THIS BOOKMaking Enterprise Information Management (EIM) Work for Business

Making Enterprise Information Management (EIM) Work for Business: A Guide to Understanding Information as an Asset provides a comprehensive discussion of EIM. It endeavors to explain information asset management and place it into a pragmatic, focused, and relevant light. The book is organized into two parts. Part 1 provides the material required to sell, understand, and validate the EIM program. It explains concepts such as treating Information, Data, and Content as true assets; information management maturity; and how EIM affects organizations.

Corporate Data Quality

Corporate Data Quality
  • Author : Boris Otto,Hubert Österle
  • Publisher : epubli
  • Release : 08 December 2015
GET THIS BOOKCorporate Data Quality

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

Telling Your Data Story

Telling Your Data Story
  • Author : Scott Taylor
  • Publisher : Unknown Publisher
  • Release : 15 November 2020
GET THIS BOOKTelling Your Data Story

The Data Whisperer's practical guide to explaining and understanding the strategic value of data management. The need for data management is everywhere across your company. The value of every digitally transformative customer-facing initiative, every data science and analytics-based project, every as-a-service offering, every foray into e-commerce, and every enterprise software implementation is inextricably linked to the successful output of data management efforts. Although it is a simple function of garbage in garbage out, that slogan rarely drives any sustainable executive

Entity Resolution and Information Quality

Entity Resolution and Information Quality
  • Author : John R. Talburt
  • Publisher : Elsevier
  • Release : 14 January 2011
GET THIS BOOKEntity Resolution and Information Quality

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