Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
Author: Laura Sebastian-Coleman
Publsiher: Newnes
Total Pages: 376
Release: 2012-12-31
ISBN 10: 0123977541
ISBN 13: 9780123977540
Language: EN, FR, DE, ES & NL

Measuring Data Quality for Ongoing Improvement Book Review:

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

The Practitioner s Guide to Data Quality Improvement

The Practitioner s Guide to Data Quality Improvement
Author: David Loshin
Publsiher: Elsevier
Total Pages: 432
Release: 2010-11-22
ISBN 10: 9780080920344
ISBN 13: 0080920349
Language: EN, FR, DE, ES & NL

The Practitioner s Guide to Data Quality Improvement Book Review:

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.

Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Elsevier
Total Pages: 352
Release: 2008-09-01
ISBN 10: 0080558399
ISBN 13: 9780080558394
Language: EN, FR, DE, ES & NL

Executing Data Quality Projects Book Review:

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 Assessment

Data Quality Assessment
Author: Arkady Maydanchik
Publsiher: Technics Publications
Total Pages: 336
Release: 2007-04-01
ISBN 10: 163462047X
ISBN 13: 9781634620475
Language: EN, FR, DE, ES & NL

Data Quality Assessment Book Review:

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’s 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, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn’t have to be such a mammoth task. DATA QUALITY ASSESSMENT is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors – the first step in any data quality program. Master techniques in: • Data profiling and gathering metadata • Identifying, designing, and implementing data quality rules • Organizing rule and error catalogues • Ensuring accuracy and completeness of the data quality assessment • Constructing the dimensional data quality scorecard • Executing a recurrent data quality assessment 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. David Wells, Director of Education, Data Warehousing Institute

Navigating the Labyrinth

Navigating the Labyrinth
Author: Laura Sebastian-Coleman
Publsiher: Technics Publications
Total Pages: 208
Release: 2018-05-09
ISBN 10: 1634623770
ISBN 13: 9781634623773
Language: EN, FR, DE, ES & NL

Navigating the Labyrinth Book Review:

An Executive Guide to Data Management

Data Quality

Data Quality
Author: Jack E. Olson
Publsiher: Elsevier
Total Pages: 300
Release: 2003-01-09
ISBN 10: 0080503691
ISBN 13: 9780080503691
Language: EN, FR, DE, ES & NL

Data Quality Book Review:

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.

Journey to Data Quality

Journey to Data Quality
Author: Yang W. Lee,Leo L. Pipino,Richard Y. Wang,James D. Funk
Publsiher: Mit Press
Total Pages: 226
Release: 2009
ISBN 10: 9780262513357
ISBN 13: 0262513358
Language: EN, FR, DE, ES & NL

Journey to Data Quality Book Review:

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 by practitioners, executives, and students for planning and implementing a viable data and information quality management program. This practical guide, based on rigorous research and informed by real-world examples, describes the challenges of data management and provides the principles, strategies, tools, and techniques necessary to meet them. The authors, all leaders in the data quality field for many years, discuss how to make the economic case for data quality and the importance of getting an organization's leaders on board. They outline different approaches for assessing data, both subjectively (by users) and objectively (using sampling and other techniques). They describe real problems and solutions, including efforts to find the root causes of data quality problems at a healthcare organization and data quality initiatives taken by a large teaching hospital. They address setting company policy on data quality and, finally, they consider future challenges on the journey to data quality.

Data Quality

Data Quality
Author: Carlo Batini,Monica Scannapieco
Publsiher: Springer Science & Business Media
Total Pages: 262
Release: 2006-09-27
ISBN 10: 3540331735
ISBN 13: 9783540331735
Language: EN, FR, DE, ES & NL

Data Quality Book Review:

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.

Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Academic Press
Total Pages: 376
Release: 2021-05-27
ISBN 10: 0128180161
ISBN 13: 9780128180167
Language: EN, FR, DE, ES & NL

Executing Data Quality Projects Book Review:

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

Handbook of EHealth Evaluation

Handbook of EHealth Evaluation
Author: Francis Yin Yee Lau,Craig Kuziemsky
Publsiher: Unknown
Total Pages: 487
Release: 2016-11
ISBN 10: 9781550586015
ISBN 13: 1550586017
Language: EN, FR, DE, ES & NL

Handbook of EHealth Evaluation Book Review:

To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/

Data Quality

Data Quality
Author: Rupa Mahanti
Publsiher: Quality Press
Total Pages: 526
Release: 2019-03-18
ISBN 10: 0873899776
ISBN 13: 9780873899772
Language: EN, FR, DE, ES & NL

Data Quality Book Review:

“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 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.

Exploratory Data Mining and Data Cleaning

Exploratory Data Mining and Data Cleaning
Author: Tamraparni Dasu,Theodore Johnson
Publsiher: John Wiley & Sons
Total Pages: 203
Release: 2003-08-01
ISBN 10: 0471458643
ISBN 13: 9780471458647
Language: EN, FR, DE, ES & NL

Exploratory Data Mining and Data Cleaning Book Review:

Written for practitioners of data mining, data cleaning anddatabase management. Presents a technical treatment of data quality includingprocess, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through aniterative data exploration loop and incorporation of domainknowledge. Addresses methods of detecting, quantifying and correcting dataquality issues that can have a significant impact on findings anddecisions, using commercially available tools as well as newalgorithmic approaches. Uses case studies to illustrate applications in real lifescenarios. Highlights new approaches and methodologies, such as theDataSphere space partitioning and summary based analysistechniques. Exploratory Data Mining and Data Cleaning will serve as animportant reference for serious data analysts who need to analyzelarge amounts of unfamiliar data, managers of operations databases,and students in undergraduate or graduate level courses dealingwith large scale data analys is and data mining.

Multi Domain Master Data Management

Multi Domain Master Data Management
Author: Mark Allen,Dalton Cervo
Publsiher: Morgan Kaufmann
Total Pages: 244
Release: 2015-03-21
ISBN 10: 0128011475
ISBN 13: 9780128011478
Language: EN, FR, DE, ES & NL

Multi Domain Master Data Management Book Review:

Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data.

Meeting the Challenges of Data Quality Management

Meeting the Challenges of Data Quality Management
Author: Laura Sebastian-Coleman
Publsiher: Academic Press
Total Pages: 250
Release: 2022-02-15
ISBN 10: 9780128217375
ISBN 13: 0128217375
Language: EN, FR, DE, ES & NL

Meeting the Challenges of Data Quality Management Book Review:

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book introduces new and experienced DQ practitioners with the understanding they need to communicate with stakeholders in a consistent manner. Sections cover practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. The book extends these ideas to the management of data quality within "big data” environments. It will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, and in academia and government organizations. Users will find this to be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses. Outlines a set of challenges that are inherent in the process of managing the quality of data, including the rapid evolution of technology and the effort of organizations to take advantage of technological change Investigates approaches to meeting the challenges that get in the way of data quality management and enables professionals to build out the components that contribute to a successful data quality management program Combines a discussion of fundamentals with their practical application which should enable university students to see the direct connection between theory and practice Includes appendices that feature DQ maturity models as well as the Data Profiling Analysis Template (DATA PAT) for capturing results from executing data profiling

Robust Quality

Robust Quality
Author: Rajesh Jugulum
Publsiher: CRC Press
Total Pages: 124
Release: 2018-09-03
ISBN 10: 0429877269
ISBN 13: 9780429877261
Language: EN, FR, DE, ES & NL

Robust Quality Book Review:

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies. Features: Integrates data science, analytics and process engineering concepts Discusses how to create value by considering data, analytics and processes Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches Reviews a structured approach for analytics execution

Business Information Systems

Business Information Systems
Author: Witold Abramowicz,Rafael Corchuelo
Publsiher: Springer
Total Pages: 541
Release: 2019-06-18
ISBN 10: 3030204855
ISBN 13: 9783030204853
Language: EN, FR, DE, ES & NL

Business Information Systems Book Review:

The two-volume set LNBIP 353 and 354 constitutes the proceedings of the 22nd International Conference on Business Information Systems, BIS 2019, held in Seville, Spain, in June 2019. The theme of the BIS 2019 was "Data Science for Business Information Systems", inspiring researchers to share theoretical and practical knowledge of the different aspects related to Data Science in enterprises. The 67 papers presented in these proceedings were carefully reviewed and selected from 223 submissions. The contributions were organized in topical sections as follows: Part I: Big Data and Data Science; Artificial Intelligence; ICT Project Management; and Smart Infrastructure. Part II: Social Media and Web-based Systems; and Applications, Evaluations and Experiences.

Information Management

Information Management
Author: William McKnight
Publsiher: Newnes
Total Pages: 214
Release: 2013-11-30
ISBN 10: 0124095267
ISBN 13: 9780124095267
Language: EN, FR, DE, ES & NL

Information Management Book Review:

Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. Balances business and technology, with non-product-specific technical detail Shows how to leverage data to deliver ROI for a business Engaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions

Work Measurement and Methods Improvement

Work Measurement and Methods Improvement
Author: Lawrence S. Aft
Publsiher: John Wiley & Sons
Total Pages: 464
Release: 2000-02-25
ISBN 10: 9780471370895
ISBN 13: 0471370894
Language: EN, FR, DE, ES & NL

Work Measurement and Methods Improvement Book Review:

Practical, up-to-date coverage for a new generation of engineering and management professionals. Lawrence S. Aft s Productivity, Measurement, and Improvement has long served as a seminal reference for students and professionals in industrial engineering, quality management, and other related fields. Now Work Measurement and Methods Improvement brings his work right up to date with the demands of today s rapidly changing marketplace, where work measurement and methods improvement have a vital role to play in improving quality and enhancing productivity in a wide range of industries. Accessible and easy to follow, this book presents solid, practical coverage of the key principles and practices of work measurement. It explains the purpose, use, advantages, and limitations of tools and methods for: * Work analysis including graphical productivity analysis and work methods improvement * Product measurement from time study and standard data systems to work sampling and labor reporting issues * Product improvement ergonomics, incentive systems, continuous improvement, process improvement, and more With straightforward examples, chapter-end summaries, review questions, and practice exercises that emphasize the application of fundamental concepts, Work Measurement and Methods Improvement is an essential reference for current and future professionals who must do the work and manage the process to achieve better quality, higher productivity, and powerhouse performance for their organization.

Information Quality

Information Quality
Author: Richard Y Wang,Elizabeth M. Pierce,Stuart Madnick,Craig W Fisher
Publsiher: Routledge
Total Pages: 280
Release: 2014-12-18
ISBN 10: 1317467981
ISBN 13: 9781317467984
Language: EN, FR, DE, ES & NL

Information Quality Book Review:

Organizations today have access to vast stores of data that come in a wide variety of forms and may be stored in places ranging from file cabinets to databases, and from library shelves to the Internet. The enormous growth in the quantity of data, however, has brought with it growing problems with the quality of information, further complicated by the struggles many organizations are experiencing as they try to improve their systems for knowledge management and organizational memory. Failure to manage information properly, or inaccurate data, costs businesses billions of dollars each year. This volume presents cutting-edge research on information quality. Part I seeks to understand how data can be measured and evaluated for quality. Part II deals with the problem of ensuring quality while processing data into information a company can use. Part III presents case studies, while Part IV explores organizational issues related to information quality. Part V addresses issues in information quality education.

Measuring Quality Improvement in Healthcare

Measuring Quality Improvement in Healthcare
Author: Raymond G. Carey,Robert C. Lloyd
Publsiher: Quality Press
Total Pages: 194
Release: 1995-01-01
ISBN 10: 0527762938
ISBN 13: 9780527762933
Language: EN, FR, DE, ES & NL

Measuring Quality Improvement in Healthcare Book Review:

This ground-breaking book addresses the critical, growing need among health care administrators and practitioners to measure the effectiveness of quality improvement efforts. Written by respected healthcare quality professionals, Measuring Quality Improvement in Healthcare covers practical applications of the tools and techniques of statistical process control (SPC), including control charts, in healthcare settings. The authors' straightforward discussions of data collection, variation, and process improvement set the context for the use and interpretation of control charts. Their approach incorporates "the voice of the customer" as a key element driving the improvement processes and outcomes. The core of the book is a set of 12 case studies that show how to apply statistical thinking to health care process, and when and how to use different types of control charts. The practical, down-to-earth orientation of the book makes it accessible to a wide readership.