Measuring data quality for ongoing improvement : (Record no. 24528)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04160cam a2200349 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 17577287 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190707224437.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130102s2013 maua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2012039039 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780123970336 (pbk.) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Transcribing agency | |
| Description conventions | rda |
| Modifying agency | DLC |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D35 |
| Item number | S43 2013 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 23 |
| Classification number | 005.73/C67 |
| 084 ## - OTHER CLASSIFICATION NUMBER | |
| Classification number | CBA |
| 085 00 - SYNTHESIZED CLASSIFICATION NUMBER COMPONENTS | |
| Number where instructions are found-single number or beginning number of span | CBA 005.73/C67 |
| -- | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Coleman, Laura Sebastian. |
| 245 10 - TITLE STATEMENT | |
| Title | Measuring data quality for ongoing improvement : |
| Remainder of title | a data quality assessment framework / |
| Statement of responsibility, etc. | Laura Sebastian-Coleman. |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Burlington : |
| Name of producer, publisher, distributor, manufacturer | Elsevier, |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxxix, 324 pages : |
| Other physical details | illustrations ; |
| Dimensions | 24 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references (pages 313-318) and index. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Concepts and definitions -- DQAF concepts and measurement types -- Data assessment scenarios -- Applying the DQAF to data requirements -- A strategic approach to data quality -- The DQAF in dept. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| Expansion of summary note | Shows you how to measure and monitor data quality, ensuring quality over time. This title demonstrates how to leverage a technology independent data quality measurement framework for specific business priorities and data quality challenges. It enables discussions between business and IT with a non-technical vocabulary for data quality measurement. |
| 520 ## - SUMMARY, ETC. | |
| Expansion of summary note | 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 |
| 521 ## - TARGET AUDIENCE NOTE | |
| Target audience note | College of Business and Accountancy |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Data structures (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Databases |
| General subdivision | Quality control. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| a | 7 |
| b | cbc |
| c | orignew |
| d | 1 |
| e | ecip |
| f | 20 |
| g | y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Books |
No items available.
