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The practitioner's guide to data quality improvement /
Business problems are directly related to missed data quality expectations. Flawed information production processes introduce risks preventing the successful achievement of critical business objectives. However, these flaws are mitigated through data quality management and control: controlling the q...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington, MA :
Morgan Kaufmann,
©2011.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Loshin, David, |d 1963- | |
| 245 | 1 | 4 | |a The practitioner's guide to data quality improvement / |c David Loshin. |
| 260 | |a Burlington, MA : |b Morgan Kaufmann, |c ©2011. | ||
| 300 | |a 1 online resource (xxiii, 398 pages) : |b illustrations | ||
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| 520 | |a Business problems are directly related to missed data quality expectations. Flawed information production processes introduce risks preventing the successful achievement of critical business objectives. However, these flaws are mitigated through data quality management and control: controlling the quality of the information production process from beginning to end to ensure that any imperfections are identified early, prioritized, and remediated before material impacts can be incurred. The Practitioner's Guide to Data Quality Improvement 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. This book shares templates and processes for business impact analysis, defining data quality metrics, inspection and monitoring, remediation, and using data quality tools. Never shying away from the difficult topics or subjects, this is the seminal book that offers advice on how to actually get the job done. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. | ||
| 505 | 0 | |a Business impacts of poor data quality -- The organizational data quality program -- Data quality maturity -- Enterprise initiative integration -- Developing a business case and a data quality road map -- Metrics and performance improvement -- Data governance -- Dimensions of data quality -- Data requirement analysis -- Metadata and data standards -- Data quality assessment -- Remediation and improvement planning -- Data quality service level agreements -- Data profiling -- Parsing and standardization -- Entity identity resolution -- Inspection, monitoring, auditing, and tracking -- Data enhancement -- Master data management and data quality -- Bringing it all together. | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Database management |x Quality control. | |
| 650 | 0 | |a Databases |x Quality control. | |
| 650 | 6 | |a Bases de données |x Gestion |x Qualité |x Contrôle. | |
| 650 | 7 | |a COMPUTERS |x Desktop Applications |x Databases. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Database Management |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x System Administration |x Storage & Retrieval. |2 bisacsh | |
| 650 | 7 | |a Database management |x Quality control. |2 fast |0 (OCoLC)fst00888048 | |
| 650 | 7 | |a Databases |x Quality control. |2 fast |0 (OCoLC)fst00888074 | |
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