Executing Data Quality Projects Book

Executing Data Quality Projects


  • Author : Danette McGilvray
  • Publisher : Academic Press
  • Release Date : 2021-05-27
  • Genre: Computers
  • Pages : 376
  • ISBN 10 : 9780128180167

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Executing Data Quality Projects Excerpt :

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 d

Executing Data Quality Projects Book
Score: 5
From 1 Ratings

Executing Data Quality Projects


  • Author : Danette McGilvray
  • Publisher : Elsevier
  • Release Date : 2008-09-01
  • Genre: Computers
  • Pages : 352
  • ISBN 10 : 9780080558394

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Executing Data Quality Projects Excerpt :

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.

Executing Data Quality Projects Book
Score: 5
From 1 Ratings

Executing Data Quality Projects


  • Author : Danette McGilvray
  • Publisher : Morgan Kaufmann
  • Release Date : 2008
  • Genre: Business & Economics
  • Pages : 325
  • ISBN 10 : 0123743699

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Executing Data Quality Projects Excerpt :

Introduces a systematic, effective approach to enhancing and creating data and information quality that integrates a conceptual framework with essential tools, techniques, and instructions, accompanied by helpful templates, real-world examples, and advice, as well as highlighted definitions, key concepts, checkpoints, warnings, communication activities, and best practices. Original. (Intermediate)

The Practitioner s Guide to Data Quality Improvement Book

The Practitioner s Guide to Data Quality Improvement


  • Author : David Loshin
  • Publisher : Elsevier
  • Release Date : 2010-11-22
  • Genre: Computers
  • Pages : 432
  • ISBN 10 : 0080920349

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The Practitioner s Guide to Data Quality Improvement Excerpt :

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.

Data Quality Assessment Book

Data Quality Assessment


  • Author : Arkady Maydanchik
  • Publisher : Technics Publications
  • Release Date : 2007-04-01
  • Genre: Computers
  • Pages : 336
  • ISBN 10 : 9781634620475

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Data Quality Assessment Excerpt :

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 o

Measuring Data Quality for Ongoing Improvement Book
Score: 5
From 1 Ratings

Measuring Data Quality for Ongoing Improvement


  • Author : Laura Sebastian-Coleman
  • Publisher : Newnes
  • Release Date : 2012-12-31
  • Genre: Computers
  • Pages : 376
  • ISBN 10 : 9780123977540

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Measuring Data Quality for Ongoing Improvement Excerpt :

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

Data Stewardship Book

Data Stewardship


  • Author : David Plotkin
  • Publisher : Newnes
  • Release Date : 2013-09-16
  • Genre: Computers
  • Pages : 248
  • ISBN 10 : 9780124104457

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Data Stewardship Excerpt :

Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company’s data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward’s time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards

The Decision Model Book

The Decision Model


  • Author : Barbara von Halle
  • Publisher : CRC Press
  • Release Date : 2009-10-27
  • Genre: Computers
  • Pages : 553
  • ISBN 10 : 1420082825

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The Decision Model Excerpt :

In the current fast-paced and constantly changing business environment, it is more important than ever for organizations to be agile, monitor business performance, and meet with increasingly stringent compliance requirements. Written by pioneering consultants and bestselling authors with track records of international success, The Decision Model: A Business Logic Framework Linking Business and Technology provides a platform for rethinking how to view, design, execute, and govern business logic. The book explains how to implement the Decision Model, a stable, rigorous model of core business logic that informs current and emerging technology. The authors supply a strong theoretical foundation, while succinctly defining the path needed to incorporate agile and iterative techniques for developing a model that will be the cornerstone for continual growth. Because the book introduces a new model with tentacles in many disciplines, it is divided into three sections: Section 1: A Complete overview of the Decision Model and its place in the business and technology world Section 2: A Detailed treatment of the foundation of the Decision Model and a formal definition of the Model Section 3: Specialized topics of interest on the Decision Model, including both business and technical issues The Decision Model provides a framework for organizing business rules into well-formed decision-based structures that are predictable, stable, maintainable, and normalized. More than this, the Decision Model directly correlates business logic to the business drivers behind it, allowing it to be used as a lever for meeting changing business objectives and marketplace demands. This book not only defines the Decision Model and but also demonstrates how it can be used to organize decision structures for maximum stability, agility, and technology independence and provide input into automation design.

Data Governance Book
Score: 4
From 1 Ratings

Data Governance


  • Author : John Ladley
  • Publisher : Academic Press
  • Release Date : 2019-11-08
  • Genre: Computers
  • Pages : 350
  • ISBN 10 : 9780128158326

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Data Governance Excerpt :

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. Incorporates industry changes, lessons learned and new approaches Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations Includes new case studies which detail real-world situations Explores all of the capabilities an organization must adopt to become data driven Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities Offers readers insights into ho

A Guide to the Project Management Body of Knowledge  PMBOK   Guide      Seventh Edition and The Standard for Project Management  RUSSIAN  Book

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 Date : 2021-08-01
  • Genre: Business & Economics
  • Pages : 368
  • ISBN 10 : 9781628257007

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A Guide to the Project Management Body of Knowledge PMBOK Guide Seventh Edition and The Standard for Project Management RUSSIAN Excerpt :

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 edition of the PMBOK&® Guide:•Reflects the full range of development approaches (predictive, adaptive, hybrid, etc.);•Provides an entire section devoted to tailoring the development approach and processes;•Includes an expanded list of models, methods, and artifacts;•Focuses on not just delivering project outputs but also enabling outcomes; and• Integrates with PMIstandards+™ for information and standards application content based on project type, development approach, and industry sector.

Journey to Data Quality Book

Journey to Data Quality


  • Author : Yang W. Lee
  • Publisher : Mit Press
  • Release Date : 2009
  • Genre: Computers
  • Pages : 226
  • ISBN 10 : 0262513358

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Journey to Data Quality Excerpt :

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.

MASTER DATA MANAGEMENT AND DATA GOVERNANCE  2 E Book

MASTER DATA MANAGEMENT AND DATA GOVERNANCE 2 E


  • Author : Alex Berson
  • Publisher : McGraw Hill Professional
  • Release Date : 2010-12-06
  • Genre: Computers
  • Pages : 512
  • ISBN 10 : 9780071744591

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MASTER DATA MANAGEMENT AND DATA GOVERNANCE 2 E Excerpt :

The latest techniques for building a customer-focused enterprise environment "The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance

Python for Data Analysis Book
Score: 5
From 1 Ratings

Python for Data Analysis


  • Author : Wes McKinney
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2017-09-25
  • Genre: Computers
  • Pages : 550
  • ISBN 10 : 9781491957615

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Python for Data Analysis Excerpt :

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Data Quality Book
Score: 1
From 1 Ratings

Data Quality


  • Author : Rupa Mahanti
  • Publisher : Quality Press
  • Release Date : 2019-03-18
  • Genre: Business & Economics
  • Pages : 526
  • ISBN 10 : 9780873899772

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Data Quality Excerpt :

“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.

Ten Steps to a Results Based Monitoring and Evaluation System Book

Ten Steps to a Results Based Monitoring and Evaluation System


  • Author : Jody Zall Kusek
  • Publisher : World Bank Publications
  • Release Date : 2004-06-15
  • Genre: Business & Economics
  • Pages : 264
  • ISBN 10 : 9780821389072

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Ten Steps to a Results Based Monitoring and Evaluation System Excerpt :

This Handbook provides a comprehensive ten-step model that will help guide development practitioners through the process of designing and building a results-based monitoring and evaluation system.