DATA QUALITY ENGINEERING: A SYSTEMS ENGINEERING “ENABLER”
The DQE methodology provides a proven means for designing and implementing a workable migration strategy that features incremental improvement in system operations, implementation of a working data repository, and, most important, restored quality in the data values maintained in database files.
Abstract
This paper briefly describes the circumstances that led to “legacy” automated data systems and characterizes the problems faced by today's large organizations as they grapple with escalating system maintenance costs, redundancy in data collection and maintenance efforts, and inadequate data quality. Some have developed a legacy system migration strategy that specifies, among other things, a requirement for integrated systems, common data standards, and a data repository system. However, most have had limited success at best in implementing practical, affordable solutions. This paper describes a successful approach out of the legacy morass—a rigorous methodology called “data quality engineering” (DQE). The DQE methodology provides a proven means for designing and implementing a workable migration strategy that features incremental improvement in system operations, implementation of a working data repository, and, most important, restored quality in the data values maintained in database files.