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

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
Author: Laura Sebastian-Coleman
Publsiher: Morgan Kaufmann
Total Pages: 376
Release: 2013-01-25
ISBN 10: 9780123970336
ISBN 13: 0123970334
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

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
Author: Laura Sebastian-Coleman
Publsiher: Morgan Kaufmann
Total Pages: 376
Release: 2013-01-25
ISBN 10: 9780123970336
ISBN 13: 0123970334
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

Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
Author: Laura Sebastian-Coleman
Publsiher: Morgan Kaufmann
Total Pages: 376
Release: 2013-01-25
ISBN 10: 9780123970336
ISBN 13: 0123970334
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.

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 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

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

Data Quality
Author: Jack E. Olson
Publsiher: Elsevier
Total Pages: 300
Release: 2003-01-09
ISBN 10: 9780080503691
ISBN 13: 0080503691
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.

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.

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.

Master Data Management in Practice

Master Data Management in Practice
Author: Dalton Cervo,Mark Allen
Publsiher: John Wiley & Sons
Total Pages: 272
Release: 2011-07-05
ISBN 10: 0470910550
ISBN 13: 9780470910559
Language: EN, FR, DE, ES & NL

Master Data Management in Practice Book Review:

In this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You'll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM.

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 Governance

Data Governance
Author: Neera Bhansali
Publsiher: CRC Press
Total Pages: 268
Release: 2013-06-17
ISBN 10: 1439879141
ISBN 13: 9781439879146
Language: EN, FR, DE, ES & NL

Data Governance Book Review:

As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are quickly gaining prominence. And, although data management issues have traditionally been addressed by IT departments, organizational issues critical to successful data management require the implementation of enterprise-wide accountabilities and responsibilities. Data Governance: Creating Value from Information Assets examines the processes of using data governance to manage data effectively. Addressing the complete life cycle of effective data governance—from metadata management to privacy and compliance—it provides business managers, IT professionals, and students with an integrated approach to designing, developing, and sustaining an effective data governance strategy. Explains how to align data governance with business goals Describes how to build successful data stewardship with a governance framework Outlines strategies for integrating IT and data governance frameworks Supplies business-driven and technical perspectives on data quality management, metadata management, data access and security, and data lifecycle The book summarizes the experiences of global experts in the field and addresses critical areas of interest to the information systems and management community. Case studies from healthcare and financial sectors, two industries that have successfully leveraged the potential of data-driven strategies, provide further insights into real-time practice. Facilitating a comprehensive understanding of data governance, the book addresses the burning issue of aligning data assets to both IT assets and organizational strategic goals. With a focus on the organizational, operational, and strategic aspects of data governance, the text provides you with the understanding required to leverage, derive, and sustain maximum value from the informational assets housed in your IT infrastructure.

Writing Effective Business Rules

Writing Effective Business Rules
Author: Graham Witt
Publsiher: Elsevier
Total Pages: 340
Release: 2012
ISBN 10: 0123850517
ISBN 13: 9780123850515
Language: EN, FR, DE, ES & NL

Writing Effective Business Rules Book Review:

The world of rules -- How rules work -- A brief history of rules -- Types of rules -- The building blocks of natural language rule statements -- Fact Models -- How to write quality natural language rule statements -- An end-to-end rule management methodology -- Rule statement templates and subtemplates.

The Goal

The Goal
Author: Eliyahu M. Goldratt,Jeff Cox
Publsiher: Routledge
Total Pages: 374
Release: 2016-08-12
ISBN 10: 1351982117
ISBN 13: 9781351982115
Language: EN, FR, DE, ES & NL

The Goal Book Review:

Alex Rogo is a harried plant manager working ever more desperately to try and improve performance. His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant - or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a colleague from student days - Jonah - to help him break out of conventional ways of thinking to see what needs to be done. Described by Fortune as a 'guru to industry' and by Businessweek as a 'genius', Eliyahu M. Goldratt was an internationally recognized leader in the development of new business management concepts and systems. This 20th anniversary edition includes a series of detailed case study interviews by David Whitford, Editor at Large, Fortune Small Business, which explore how organizations around the world have been transformed by Eli Goldratt's ideas. The story of Alex's fight to save his plant contains a serious message for all managers in industry and explains the ideas which underline the Theory of Constraints (TOC) developed by Eli Goldratt. Written in a fast-paced thriller style, The Goal is the gripping novel which is transforming management thinking throughout the Western world. It is a book to recommend to your friends in industry - even to your bosses - but not to your competitors!

Executing Data Quality Projects

Executing Data Quality Projects
Author: Danette McGilvray
Publsiher: Academic Press
Total Pages: 420
Release: 2021-03-15
ISBN 10: 9780128180150
ISBN 13: 0128180153
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. ~

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.

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.

The Health Care Data Guide

The Health Care Data Guide
Author: Lloyd P. Provost,Sandra Murray
Publsiher: John Wiley & Sons
Total Pages: 480
Release: 2011-12-06
ISBN 10: 1118085884
ISBN 13: 9781118085882
Language: EN, FR, DE, ES & NL

The Health Care Data Guide Book Review:

The Health Care Data Guide is designed to help students and professionals build a skill set specific to using data for improvement of health care processes and systems. Even experienced data users will find valuable resources among the tools and cases that enrich The Health Care Data Guide. Practical and step-by-step, this book spotlights statistical process control (SPC) and develops a philosophy, a strategy, and a set of methods for ongoing improvement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for a wide range of applications including evaluating current process performance, searching for ideas for and determining evidence of improvement, and tracking and documenting sustainability of improvement. A comprehensive overview of graphical methods in SPC includes Shewhart charts, run charts, frequency plots, Pareto analysis, and scatter diagrams. Other topics include stratification and rational sub-grouping of data and methods to help predict performance of processes. Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data. Companion Web site: www.josseybass.com/go/provost