Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework
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| Title: | Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework |
|---|---|
| Description: | 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 |
| Authors: | Laura Sebastian-Coleman |
| Resource Type: | eBook. |
| Subjects: | Data structures (Computer science), Databases--Quality control, Database management |
| Categories: | COMPUTERS / Data Science / General, COMPUTERS / Database Administration & Management |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework – Name: Abstract Label: Description Group: Ab Data: 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 – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Laura+Sebastian-Coleman%22">Laura Sebastian-Coleman</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+structures+%28Computer+science%29%22">Data structures (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Databases--Quality+control%22">Databases--Quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+General%22">COMPUTERS / Data Science / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Database+Administration+%26+Management%22">COMPUTERS / Database Administration & Management</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 001.4 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Data structures (Computer science) Type: general – SubjectFull: Databases--Quality control Type: general – SubjectFull: Database management Type: general Titles: – TitleFull: Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Laura Sebastian-Coleman – PersonEntity: Name: NameFull: Laura Sebastian-Coleman IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2013 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9780123970336 – Type: isbn-electronic Value: 9780123977540 Titles: – TitleFull: Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework Type: main |
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