The practitioner's guide to data quality improvement [electronic book] / David Loshin.

By:

Loshin, David, 1963-

Contributor(s):

ScienceDirect (Online service)

Material type: Text

TextPublication details: Burlington, MA : Morgan Kaufmann, c2011.Description: xxiii, 398 p. : ill. ; 24 cmISBN:

0123737176 (electronic bk.)
9780123737175 (electronic bk.)

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Print version:: Practitioner's guide to data quality improvement.Online resources:

ScienceDirect eBook

Contents:

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.

Summary: 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.

No physical items for this record

Enhanced descriptions from Syndetics:

The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It 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.

It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.

This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers.

Includes index.

Includes bibliographical references and index.

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.

Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2010. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Dec. 8, 2010). Access may be restricted to users at subscribing institutions.

Table of contents provided by Syndetics

Preface
Chapter 1 Business Impacts of Poor Data Quality
Chapter 2 The Organizational Data Quality Program
Chapter 3 Data Quality Maturity
Chapter 4 Enterprise Initiative Integration
Chapter 5 Developing a Business Case and a Data Quality Roadmap
Chapter 6 Metrics and Performance Improvement
Chapter 7 Data Governance
Chapter 8 Dimensions of Data Quality
Chapter 9 Data Requirement Analysis
Chapter 10 Metadata and Data Standard
Chapter 11 Data Quality Assessment
Chapter 12 Remediation and Improvement Planning
Chapter 13 Data Quality Service Level Agreements
Chapter 14 Data Profiling
Chapter 15 Parsing and Standardization
Chapter 16 Entity Identity Resolution
Chapter 17 Inspection, Monitoring, Auditing, and Tracking
Chapter 18 Data Enhancement
Chapter 19 Master Data Management and Data Quality
Chapter 20 Bringing It All Together

Author notes provided by Syndetics

David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including "Master Data Management" (2008) and "Business Intelligence - The Savvy Manager's Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.