|Author||: David Loshin|
|Release Date||: 2010-11-22|
|ISBN 10||: 9780080920344|
|Pages||: 432 pages|
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.
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
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.
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
Data governance looks simple on paper, but in reality it is a complex issue facing organizations. In this practical guide, data experts Uma Gupta and San Cannon look to demystify data governance through pragmatic advice based on real-world experience and cutting-edge academic research.
|Author||: Randy Bartlett|
|Publisher||: McGraw Hill Professional|
|Release Date||: 2013-01-25|
|ISBN 10||: 0071807608|
|Pages||: 256 pages|
Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.
|Author||: Julie K. Johnson,William A. Sollecito|
|Publisher||: Jones & Bartlett Learning|
|Release Date||: 2018-10-01|
|ISBN 10||: 1284126595|
|Pages||: 650 pages|
Through a unique interdisciplinary perspective on quality management in health care, this text covers the subjects of operations management, organizational behavior, and health services research. With a particular focus on Total Quality Management (TQM) and Continuous Quality Improvement (CQI), the challenges of implementation and institutionalization are addressed using examples from a variety of health care organizations, including primary care clinics, hospital laboratories, public health departments, and academic health centers. Significantly revised throughout, the Fifth Edition offers a greater focus on application techniques, and features 14 chapters in lieu of the prior edition's 20 chapters, making it an even more effective teaching tool. New chapters have been incorporated on Implementation Science (3), Lean Six Sigma (6), and Classification and the Reduction of Medical Errors (10).
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.
Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.
|Author||: Mikel J. Harry,Prem S. Mann,Ofelia C. De Hodgins,Richard L. Hulbert,Christopher J. Lacke|
|Publisher||: John Wiley & Sons|
|Release Date||: 2011-09-20|
|ISBN 10||: 1118210212|
|Pages||: 832 pages|
This hands-on book presents a complete understanding of SixSigma and Lean Six Sigma through data analysis and statisticalconcepts In today's business world, Six Sigma, or Lean Six Sigma, is acrucial tool utilized by companies to improve customersatisfaction, increase profitability, and enhance productivity.Practitioner's Guide to Statistics and Lean Six Sigma forProcess Improvements provides a balanced approach toquantitative and qualitative statistics using Six Sigma and LeanSix Sigma methodologies. Emphasizing applications and the implementation of data analysesas they relate to this strategy for business management, this bookintroduces readers to the concepts and techniques for solvingproblems and improving managerial processes using Six Sigma andLean Six Sigma. Written by knowledgeable professionals working inthe field today, the book offers thorough coverage of thestatistical topics related to effective Six Sigma and Lean SixSigma practices, including: Discrete random variables and continuous random variables Sampling distributions Estimation and hypothesis tests Chi-square tests Analysis of variance Linear and multiple regression Measurement analysis Survey methods and sampling techniques The authors provide numerous opportunities for readers to testtheir understanding of the presented material, as the real datasets, which are incorporated into the treatment of each topic, canbe easily worked with using Microsoft Office Excel, Minitab,MindPro, or Oracle's Crystal Ball software packages. Examples ofsuccessful, complete Six Sigma and Lean Six Sigma projects aresupplied in many chapters along with extensive exercises that rangein level of complexity. The book is accompanied by an extensive FTPsite that features manuals for working with the discussed softwarepackages along with additional exercises and data sets. Inaddition, numerous screenshots and figures guide readers throughthe functional and visual methods of learning Six Sigma and LeanSix Sigma. Practitioner's Guide to Statistics and Lean Six Sigma forProcess Improvements is an excellent book for courses on SixSigma and statistical quality control at the upper-undergraduateand graduate levels. It is also a valuable reference forprofessionals in the fields of engineering, business, physics,management, and finance.
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.
|Author||: Howard S. Gitlow,Richard J. Melnyck,David M. Levine|
|Publisher||: FT Press|
|Release Date||: 2015-04-08|
|ISBN 10||: 0133925455|
|Pages||: 480 pages|
Master modern Six Sigma implementation with the most complete, up-to-date guide for Green Belts, Black Belts, Champions and students! Now fully updated with the latest lean and process control applications, A Guide to Lean Six Sigma and Process Improvement for Practitioners and Students, Second Edition gives you a complete executive framework for understanding quality and implementing Lean Six Sigma. Whether you're a green belt, black belt, champion, or student, Howard Gitlow and Richard Melnyck cover all you need to know. Step by step, they systematically walk you through the five-step DMAIC implementation process, with detailed examples and many real-world case studies. You'll find practical coverage of Six Sigma statistics and management techniques, from dashboards and control charts to hypothesis testing and experiment design. Drawing on their extensive experience consulting on Six Sigma and leading major Lean and quality initiatives, Gitlow and Melnyck offer up-to-date coverage of: What Six Sigma can do, and how to manage it effectively Six Sigma roles, responsibilities, and terminology Running Six Sigma programs with Dashboards and Control Charts Mastering each DMAIC phase: Define, Measure, Analyze, Improve, Control Understanding foundational Six Sigma statistics: probability, probability distributions, sampling distributions, and interval estimation Pursuing Six Sigma Champion or Green Belt Certification, and more This guide will be an invaluable resource for everyone who is currently involved in Six Sigma implementation, or plans to be. It's ideal for students in quality programs; "Green Belts" who project manage Six Sigma implementations, "Black Belts" who lead Six Sigma teams; "Champions" who promote and coordinate Six Sigma at the executive level; and anyone seeking Six Sigma certification.