Description
Data are among the Information Age company's most important assets. Without quality data, you simply can't serve customers properly, make good decisions, or take advantage of new data warehouse and data mining technologies. Written by the inventor of many modern techniques for data quality, this comprehensive book provides you with all the information necessary to set up a data quality program, make and maintain improvements to data quality, and create a discernible business advantage. The author emphasizes practical application throughout the book's five sections - Management Roles, Technical Methods, Foundations, Case Studies, and Summaries. Data Quality For The Information Age covers all 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 and sustain improvements, and the life-cycle and dimensions of data quality. It includes case studies, summaries of main points, roles and responsibilities for each individual, and summaries of good design practices. Several groups can benefit immeasurably from this work: Chief Information Officers, Chief Financial Officers, and other executives gain a new understanding of the impact of poor data quality on their organization, and learn how to develop a policy that aligns management roles and responsibilities for data in a model improvement program. Quality Managers and Senior MIS Managers learn the how-to's of making improvements to data quality. Database Managers and Software Developers, Software Engineers, and other Information Professionals refresh and deepen their fundamental knowledge of data, information, and data quality. Features over 80 illustrations.
Table Of Contents
Why Care About Data Quality? Strategies for Improving Data Quality. Data Quality Policy. Starting and Nurturing a Data Quality Program. Process Management. Process Representation and the Functions of Information Processing Approach. Data Quality Requirements. Statistical Quality Control. Measurement Systems, Data Tracking, and Process Improvement. Just What is (or are) Data? Dimensions of Data Quality. Data Quality and Re-Engineering at AT&T. Data Quality Across the Corporation - Telstra 's Experience. Summary - Roles and Responsibilities. Glossary. Index.