Data Quality for the Information Age

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.

• 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.