The calculus computes the quality of the result, allowing the user to judge the risk involved in using the data, and evaluate alternative quality-control strategies. In the long term, our goal is to create a theory of data quality based on reference disciplines such as computer science, organizational behavior, statistics, accounting, and total quality management. This theory of data quality, in turn, may serve as a foundation for other research efforts that require quality information. In the short term, our goal is to create a center of excellence among practitioners of data quality techniques and to act as a clearinghouse for effective techniques and methods. The major components of the TDQM research framework, Dr. Wang said, are definition, analysis, and improvement.
Definition focuses on defining and measuring data quality.
Improvement involves redesigning the business and implementing new technologies to significantly upgrade the quality of corporate information. Dr. Madnick, John Norris Maguire Professor of Information Technology and Leaders for Manufacturing Professor, who is the other codirector of the TDQM research program, cited a case study in which a transportation company determined that almost 25 percent of its deliveries were delayed due to erroneous, missing, mistrusted, or inappropriate data. This resulted in unnecessary redeliveries, avoidable compensation payments, and dissatisfied and lost customers. The company instituted a comprehensive data quality improvement program, which included advanced data collection techniques. Enhanced data quality resulted in more effective deliveries, estimated cost savings of several million dollars, and the reversal of declining market share.