Data Quality Problems in Army Logistics, Classification, Examples, and Solutions

Implicit in the Army Force XXI concept is the idea that information and data are assets-as important in their own right as the Army's physical assets of personnel, vehicles, and materiel. The Army's Velocity Management initiative, which is focused on logistics, recognizes the central importance of using performance data to inform the reengineering and management of logistics processes. To truly qualify as an asset, however, data must have the requisite quality. Unfortunately, much logistics data in the Army is widely perceived to be of poor quality. This perception is based on personal experience, anecdotes, and numerous examples of failed analyses and modeling efforts that were unable to overcome data problems. The purpose of this project was to examine quality problems in Army logistics data and to recommend solutions. To focus the project, we selected a small group of data elements generated by the 'retail' Army that are transmitted to and used by centralized logistics activities in the 'wholesale' Army. Our working definition of 'bad data' is based on the current data- quality literature, which links the idea of data quality to the uses to which data are put: if a given set of reported data cannot provide the information needed for decisions, a data-quality problem exists. Our discussion of logistics data problems is grounded in the uses of the data.