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

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Rand, 1996 - Technology & Engineering - 70 pages
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Many new Army initiatives such as Velocity Management and Force XXI are based on the assumption that information will be a key asset for U.S. armed forces of the future. Many Army logistics data, however, are widely perceived to be of poor quality. In this report, the authors review the current literature on data quality, develop a three-way scheme for classifying data quality problems, and apply the classification to the analysis of an important logistics data element, the End Item Code (EIC). The authors argue that the EIC has quality problems of all three types, and review the evidence and efforts of the Army to address each. The most fundamental problem is due to the deep gap between the retail organizations that create EIC data and the wholesale organizations that use it. The authors propose several strategies to bridge the gap in order to improve the quality of the EIC data. An appendix applies the data classification scheme to a number of other important logistics data elements exhibiting data-quality problems and reaches similar conclusions about their causes.

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Data Quality Problems in Army Logistics, Classification, Examples ...
Title : Data Quality Problems in Army Logistics, Classification, Examples, and Solutions. Descriptive Note : Final rept., ...
stinet.dtic.mil/ oai/ oai?& verb=getRecord& metadataPrefix=html& identifier=ADA317605

Data Quality Problems in Army Logistics: Classification, Examples ...
DATA QUALITY. PROBLEMS IN. ARMY LOGISTICS. Classification, Examples,. and Solutions. Lionel A. Galway. ◆. Christopher H. Hanks. R. Arroyo Center ...
www.rand.org/ pubs/ monograph_reports/ 2005/ MR721.pdf

Data Quality Problems in Army Logistics, Classification, Examples ...
Data Quality Problems in Army Logistics, Classification, Examples, and Solutions. Authors: Lionel A. Galway; Christopher H. Hanks; RAND ARROYO CENTER SANTA ...
www.stormingmedia.us/ 50/ 5067/ A506713.html

About the author (1996)

Lionel A. Galway (Ph.D., Statistics, 1987, Carnegie Mellon University) is a senior statistician at RAND.

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