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Excerpt from Data Quality Requirements Analysis and Modeling
Computerworld, September 28, 1992, p. 80-84. The Wall Street Journal, May 26, 1992, page B6. Just like the well publicized concept of zero defect products in the manufacturing literature.high for the vital few projects whereas the trivial many defects are not worth tackling because the cure costs more than the disease (juran Gryna, In sum, when the cost is prohibitively high, it is not feasible to attain zero defect data.
Given that zero defect data may not always be necessary nor attainable, it would be useful to be able to judge the quality of data. This suggests that we tag data with quality indicators which are characteristics of the data and its manufacturing process. From these quality indicators, the user can make a judgment of the quality of the data for the specific application at hand. In making a financial decision to purchase stocks, for example, it would be useful to know the quality of data through quality indicators such as who originated the data, when the data was collected, and how the data was collected.
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This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Excerpt from Data Quality Requirements Analysis and Modeling
Computerworld, September 28, 1992, p. 80-84. The Wall Street Journal, May 26, 1992, page B6. Just like the well publicized concept of zero defect products in the manufacturing literature.high for the vital few projects whereas the trivial many defects are not worth tackling because the cure costs more than the disease (juran Gryna, In sum, when the cost is prohibitively high, it is not feasible to attain zero defect data.
Given that zero defect data may not always be necessary nor attainable, it would be useful to be able to judge the quality of data. This suggests that we tag data with quality indicators which are characteristics of the data and its manufacturing process. From these quality indicators, the user can make a judgment of the quality of the data for the specific application at hand. In making a financial decision to purchase stocks, for example, it would be useful to know the quality of data through quality indicators such as who originated the data, when the data was collected, and how the data was collected.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Excerpt from Data Quality Requirements Analysis and Modeling
Data Quality Requirements Analysis and Modeling was written by Richard Y. Wang and M. P. Reddy in 1992. This is a 46 page book, containing 11802 words and 11 pictures. Search Inside is enabled for this title.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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- VerlagForgotten Books
- Erscheinungsdatum2018
- ISBN 10 1332257127
- ISBN 13 9781332257126
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten44
- Kontakt zum HerstellerNicht verfügbar
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Data Quality Requirements Analysis and Modeling (Classic Reprint)
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Paperback. Zustand: New. Print on Demand. This book explores the concept of data quality, arguing that it is a multidimensional, hierarchical concept that can be applied to data at the cell level. Drawing on examples from various industries, the author presents a data quality requirements analysis methodology that can be used to identify the quality parameters and indicators that are important to a particular application. The book also introduces an attribute -based data model that can be used to store, retrieve, and process quality indicators, and presents a quality indicator algebra that can be used to manipulate quality indicators. The book concludes by discussing the importance of data quality management, and provides a set of guidelines for implementing a data quality program. The author's insights on data quality are significant because they provide a framework for understanding the concept and developing strategies for improving it. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Bestandsnummer des Verkäufers 9781332257126_0
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