Data Quality for the Information Age
Published on 1. January 1997
Book
Hardback
332 pages
978-0-89006-883-0 (ISBN)
Description
This informative book goes beyond the technical aspects of data management to provide detailed analyses of quality problems and their impacts, potential solutions and how they are combined to form an overall data quality program, senior management's role, methods used to make improvements, and the life-cycle of data quality. It concludes with case studies, summaries of main points, roles and responsibilities for each individual, and a helpful listing of "dos and don'ts".
More details
Series
Language
English
Place of publication
Norwood
United States
Target group
Professional and scholarly
Product notice
Laminated cover
Illustrations
black & white illustrations
Dimensions
Height: 241 mm
Width: 156 mm
Thickness: 27 mm
Weight
680 gr
ISBN-13
978-0-89006-883-0 (9780890068830)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Dr. Thomas C. Redman is president of the Navesink Consulting Group. He led data quality programs at AT&T and AT&T Bell Labs. He holds a Ph.D. and M.S. in statistics from Florida State University. He is a member of the American Statistical Association, holder of a patent, and is published extensively.
Content
Why Care About Data Quality: Poor Data is Pervasive. Poor Data Quality Impacts Business Success. Data Quality Can be a Source of Competitive Advantage. Strategies for Improving Data Quality: Which Data to Improve? Improving Data Accuracy for One Database. Improving DataAccuracy for Two Databases. Improving Data Accuracy in the Warehouse. Data Quality Policy: What Should a Data Policy Cover? Needed Background on Data. A Model Data Policy. Deploying the Policy. Starting and Nurturing a Data Quality Program: A Model for Successful Change. Getting Started. Growth Stages. Becoming Part of the Mainstream. The Role of Senior Management. Process Management: Future Performance of Processes. Step 1 -- Estabish a Process Owner and Management Team. Step 2 -- Describe the Process and Understand Customer Needs. Step 3 -- Establish a Measurement System. Step 4 -- Establish Statistical control and Check Conformance to Requirements. Step 5 -- Identify Improvement Opportunities. Step 6 -- Select Opportunities. Step 7 -- Make and Sustain Improvements. Process Representation and the Functions of Information Processing Approach: Basic Ideas. The Information Model/The FIP Chart. Enhancements to the Basic Information Model. Measurement and Improvement Opportunities. Data Quality Requirements: Quality Function Deployment. Data Quality Requirements for an Existing Information Chain. Data Quality Requirements at the Design Stage. Statistical Quality Control: Variation. Stable Processes. Control Limits -- Statistical Theory and Methods of SQC. Interpreting Control Charts. Conformance to Requirements. Measurement Systems, Data Tracking, and Process Improvement: Measurement Systems. Process Requirements. What to Measure. The Measuring Device and Protocol -- Data Tracking. Implementation. Just What is (or are) Data?: The Data Life-Cycle. Data Defined. Management Properties of Data. A Model of an Enterprise's Data Resource. Information. Dimensions of Data Quality: Quality Dimensions of a Conceptual View. Quality Dimensions of Data Values. Quality Dimensions of Data Representation. More on Data Consistency. Data Quality and Re-Engineering at AT&T: First Steps. Re-Engineering. Data Quality Across the Corporation -- Telstra?s Experience: Program Definition. First Steps. Full Program. Results. Summary -- Roles and Responsibilities: Roles for Leaders. Roles for Process Owners. Roles for Information Professionals. Final Remarks -- The Three Most Important Points. Glossary. Index.