Data Quality Assessment
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Data Quality Assessment
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Author : Arkady Maydanchik
language : en
Publisher:
Release Date : 2007
Data Quality Assessment written by Arkady Maydanchik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
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 is 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, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says "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."
Measuring Data Quality For Ongoing Improvement
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Author : Laura Sebastian-Coleman
language : en
Publisher: Newnes
Release Date : 2012-12-31
Measuring Data Quality For Ongoing Improvement written by Laura Sebastian-Coleman and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-31 with Computers categories.
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
Who Data Quality Assessment Of National And Partner Monitoring Data And System Implementation Tool Second Edition
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Author : World Health Organization
language : en
Publisher: World Health Organization
Release Date : 2024-04-17
Who Data Quality Assessment Of National And Partner Monitoring Data And System Implementation Tool Second Edition written by World Health Organization and has been published by World Health Organization this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-17 with Medical categories.
This technical brief summarizes key updates to the 2018 Data quality assessment of national and partner HIV treatment and patient monitoring data and systems implementation tool focusing on implementing and following up remedial activities after such assessments and guidance on developing data quality improvement strategies. It is intended that this technical update is used alongside the 2018 implementation tool to support country implementation of data quality assessments. This technical brief provides further guidance and recommendations on the following: - developing a follow-up action plan after conducting data quality assessment to support the implementation of remedial actions; - disseminating, notifying and reporting data quality assessment results; - using data quality assessment results to adjust national HIV estimates; and - implementing data quality improvement activities at the site level that link data quality assessment to broader data quality improvement activities to address data quality issues and strengthen data use.
Data Quality Assessment Report Of The Data Quality Work Group
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Author :
language : en
Publisher:
Release Date : 1980
Data Quality Assessment Report Of The Data Quality Work Group written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with categories.
The Practitioner S Guide To Data Quality Improvement
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Author : David Loshin
language : en
Publisher: Elsevier
Release Date : 2010-11-22
The Practitioner S Guide To Data Quality Improvement written by David Loshin and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-22 with Computers categories.
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 Complete Self Assessment Guide
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Author : Gerardus Blokdyk
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-05-14
Data Quality Complete Self Assessment Guide written by Gerardus Blokdyk and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-14 with categories.
What are the business goals Data Quality is aiming to achieve? Who sets the Data Quality standards? Will team members regularly document their Data Quality work? Think about the people you identified for your Data Quality project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively? Are improvement team members fully trained on Data Quality? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' For more than twenty years, The Art of Service's Self-Assessments empower people who can do just that - whether their title is marketer, entrepreneur, manager, salesperson, consultant, business process manager, executive assistant, IT Manager, CxO etc... - they are the people who rule the future. They are people who watch the process as it happens, and ask the right questions to make the process work better. This book is for managers, advisors, consultants, specialists, professionals and anyone interested in Data Quality assessment. Featuring 637 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Quality improvements can be made. In using the questions you will be better able to: - diagnose Data Quality projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Quality and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Quality Scorecard, you will develop a clear picture of which Data Quality areas need attention. Included with your purchase of the book is the Data Quality Self-Assessment downloadable resource, containing all 637 questions and Self-Assessment areas of this book. This enables ease of (re-)use and enables you to import the questions in your preferred Management or Survey Tool. Access instructions can be found in the book. You are free to use the Self-Assessment contents in your presentations and materials for customers without asking us - we are here to help. This Self-Assessment has been approved by The Art of Service as part of a lifelong learning and Self-Assessment program and as a component of maintenance of certification. Optional other Self-Assessments are available. For more information, visit http://theartofservice.com
Data Quality Assurance Module 3 Site Assessment Of Data Quality
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Author : World Health Organization
language : en
Publisher: World Health Organization
Release Date : 2023-01-17
Data Quality Assurance Module 3 Site Assessment Of Data Quality written by World Health Organization and has been published by World Health Organization this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-17 with Medical categories.
This publication is one of the three module toolkit and provide technical guidance and tools to support the work on strengthening data quality in countries. This is part of the Division of Data, Analytics and Delivery for Impact’s scope of work providing normative guidance for health information system strengthening.
Data Quality
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Author : Jack E. Olson
language : en
Publisher: Elsevier
Release Date : 2003-01-09
Data Quality written by Jack E. Olson and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-09 with Computers categories.
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.
Uncertainty Handling And Quality Assessment In Data Mining
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Author : Michalis Vazirgiannis
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-07-24
Uncertainty Handling And Quality Assessment In Data Mining written by Michalis Vazirgiannis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-24 with Business & Economics categories.
Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results. This book presents a general framework for assessing quality and handling uncertainty which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'Uminer' which is introduced to the reader for the first time. This tool supports the key data mining tasks while enhancing the traditional processes for handling uncertainty and assessing quality. Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology and telecommunications that illustrate how the tool works in 'real world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.
Meeting The Challenges Of Data Quality Management
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Author : Laura Sebastian-Coleman
language : en
Publisher: Academic Press
Release Date : 2022-01-25
Meeting The Challenges Of Data Quality Management written by Laura Sebastian-Coleman and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-25 with Computers categories.
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will 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. Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today’s digitally interconnected world Explores the five challenges in relation to organizational data, including "Big Data," and proposes approaches to meeting them Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations Provides Data Quality practitioners with ways to communicate consistently with stakeholders