Measuring Data Quality for Ongoing Improvement

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  • Publisher : Newnes
  • Release : 31 December 2012
  • ISBN : 9780123977540
  • Page : 376 pages
  • Rating : 5/5 from 1 voters

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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

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Measuring Data Quality for Ongoing Improvement

Measuring Data Quality for Ongoing Improvement
  • Author : Laura Sebastian-Coleman
  • Publisher : Newnes
  • Release Date : 2012-12-31
  • ISBN : 9780123977540
GET THIS BOOKMeasuring Data Quality for Ongoing Improvement

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

The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement
  • Author : David Loshin
  • Publisher : Elsevier
  • Release Date : 2010-11-22
  • ISBN : 0080920349
GET THIS BOOKThe Practitioner's Guide to Data Quality Improvement

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

Executing Data Quality Projects

Executing Data Quality Projects
  • Author : Danette McGilvray
  • Publisher : Academic Press
  • Release Date : 2021-05-27
  • ISBN : 9780128180167
GET THIS BOOKExecuting Data Quality Projects

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

Data Quality Assessment

Data Quality Assessment
  • Author : Arkady Maydanchik
  • Publisher : Technics Publications
  • Release Date : 2007-04-01
  • ISBN : 9781634620475
GET THIS BOOKData Quality Assessment

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

Data Governance

Data Governance
  • Author : John Ladley
  • Publisher : Academic Press
  • Release Date : 2019-11-08
  • ISBN : 9780128158326
GET THIS BOOKData Governance

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

Meeting the Challenges of Data Quality Management

Meeting the Challenges of Data Quality Management
  • Author : Laura Sebastian-Coleman
  • Publisher : Academic Press
  • Release Date : 2022-01-25
  • ISBN : 9780128217566
GET THIS BOOKMeeting the Challenges of Data Quality Management

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),

Navigating the Labyrinth

Navigating the Labyrinth
  • Author : Laura Sebastian-Coleman
  • Publisher : Technics Publications
  • Release Date : 2018-05-09
  • ISBN : 9781634623773
GET THIS BOOKNavigating the Labyrinth

An Executive Guide to Data Management

Quality in Family Practice Book of Tools

Quality in Family Practice Book of Tools
  • Author : Cheryl Levitt,Linda Hilts
  • Publisher : Unknown
  • Release Date : 2012-01
  • ISBN : 1926633385
GET THIS BOOKQuality in Family Practice Book of Tools

This Quality Book of Tools is a unique collection of quality performance indicators for primary care in Canada. Using this book will help family doctors and other primary care providers continue to improve the quality of care in their practice. Cheryl Levitt (MBBCh CCFP FCFP) is a family physician and professor in the Department of Family Medicine at McMaster University. Linda Hilts (RN BScN MEd) is a registered nurse and an assistant professor and associate member of the Department of

The Goal

The Goal
  • Author : Eliyahu M. Goldratt,Jeff Cox
  • Publisher : Routledge
  • Release Date : 2016-08-12
  • ISBN : 9781351982115
GET THIS BOOKThe Goal

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.

Robust Quality

Robust Quality
  • Author : Rajesh Jugulum
  • Publisher : CRC Press
  • Release Date : 2018-09-03
  • ISBN : 9780429877261
GET THIS BOOKRobust Quality

Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with

Data Quality

Data Quality
  • Author : Jack E. Olson
  • Publisher : Elsevier
  • Release Date : 2003-01-09
  • ISBN : 9780080503691
GET THIS BOOKData Quality

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.

Competing with High Quality Data

Competing with High Quality Data
  • Author : Rajesh Jugulum
  • Publisher : John Wiley & Sons
  • Release Date : 2014-03-10
  • ISBN : 9781118416495
GET THIS BOOKCompeting with High Quality Data

Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, butlow-quality data can actually put a company at a disadvantage. Tobe used effectively, data must accurately reflect the real-worldscenario it represents, and it must be in a form that is usable andaccessible. Quality data involves asking the right questions,targeting the correct parameters, and having an effective internalmanagement, organization, and access system. It must be relevant,complete, and correct, while falling in line with pervasiveregulatory

Master Data Management

Master Data Management
  • Author : David Loshin
  • Publisher : Morgan Kaufmann
  • Release Date : 2010-07-28
  • ISBN : 0080921213
GET THIS BOOKMaster Data Management

The key to a successful MDM initiative isn’t technology or methods, it’s people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect. Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you’ll master all the details involved in planning

Information Management

Information Management
  • Author : William McKnight
  • Publisher : Newnes
  • Release Date : 2013-11-30
  • ISBN : 9780124095267
GET THIS BOOKInformation Management

Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation

Business Information Systems

Business Information Systems
  • Author : Witold Abramowicz,Rafael Corchuelo
  • Publisher : Springer
  • Release Date : 2019-06-18
  • ISBN : 9783030204853
GET THIS BOOKBusiness Information Systems

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