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Measuring data quality for ongoing improvement: a data quality assessment framework
<i> The Data Quality Assessment Framework </i>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 o...
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| Lenguaje: | eng |
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Elsevier Science
2013
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| Acceso en línea: | http://cds.cern.ch/record/2122795 |
| _version_ | 1780949489143513088 |
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| author | Sebastian-Coleman, Laura |
| author_facet | Sebastian-Coleman, Laura |
| author_sort | Sebastian-Coleman, Laura |
| collection | CERN |
| description | <i> The Data Quality Assessment Framework </i>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 pra |
| id | cern-2122795 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2013 |
| publisher | Elsevier Science |
| record_format | invenio |
| spelling | cern-21227952021-04-21T19:52:22Zhttp://cds.cern.ch/record/2122795engSebastian-Coleman, LauraMeasuring data quality for ongoing improvement: a data quality assessment frameworkComputing and Computers<i> The Data Quality Assessment Framework </i>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 praElsevier Scienceoai:cds.cern.ch:21227952013 |
| spellingShingle | Computing and Computers Sebastian-Coleman, Laura Measuring data quality for ongoing improvement: a data quality assessment framework |
| title | Measuring data quality for ongoing improvement: a data quality assessment framework |
| title_full | Measuring data quality for ongoing improvement: a data quality assessment framework |
| title_fullStr | Measuring data quality for ongoing improvement: a data quality assessment framework |
| title_full_unstemmed | Measuring data quality for ongoing improvement: a data quality assessment framework |
| title_short | Measuring data quality for ongoing improvement: a data quality assessment framework |
| title_sort | measuring data quality for ongoing improvement: a data quality assessment framework |
| topic | Computing and Computers |
| url | http://cds.cern.ch/record/2122795 |
| work_keys_str_mv | AT sebastiancolemanlaura measuringdataqualityforongoingimprovementadataqualityassessmentframework |